This document provides an overview of social network analysis concepts including:
1. Key terms like actors, ties, relations, dyads, triads, ego networks, sociograms, and centrality measures.
2. Common network models and properties including small world networks, preferential attachment, degree distributions, and assortativity.
3. Common network analysis tasks such as link prediction, diffusion modeling, clustering, and structural analysis techniques like motif detection and blockmodeling.
This document discusses different types of knowledge and methods for knowledge acquisition. It describes declarative and procedural knowledge, as well as the knowledge acquisition paradox where experts have difficulty verbalizing their knowledge. Various knowledge acquisition methods are outlined, including observation, problem discussion, and protocol analysis. Knowledge representation techniques like rules, semantic networks, frames, and predicate logic are also introduced.
This document outlines a presentation on biological networks and the software Cytoscape. It begins with an introduction to biological networks and their taxonomy, as well as analytical approaches and visualization techniques. It then provides an overview of Cytoscape, covering core concepts like networks and tables, visual properties, and apps. The document demonstrates how to load networks and data, use visual style managers, and save and export networks. It concludes with tips and tricks for using Cytoscape and a link to a hands-on tutorial.
Keynote given at the workshop for Artificial Intelligence meets the Web of Data on Pragmatic Semantics.
In this keynote I argue that the Web of Data is a Complex System or Marketplace of Ideas rather than a classical Database, and that the model theory on which classical semantics are based is not appropriate in all situations, and propose an alternative "Pragmatic Semantics" based on optimisation of possible interpretations. .
Machine Learning from Statistical Point of ViewYury Gubman
Dr. Yury Gubman introduced himself as a statistician with experience in statistics, machine learning, and leading AI teams. He discussed how machine learning relates to statistics, with machine learning models representing statistical models estimated through different procedures. He emphasized using statistical theory to calculate additional predictive features, treat outliers, perform feature selection, and maintain models over time. He provided examples of applying these statistical approaches to crime prediction and housing price estimation problems.
In social networks, where users send messages to each other, the issue of what triggers communication between unrelated users arises: does communication between previously unrelated users depend on friend-of-a-friend type of relationships, common interests, or other factors? In this work, we study the problem of predicting directed communication
intention between two users. Link prediction is similar to communication intention in that it uses network structure for prediction. However, these two problems exhibit fundamental
differences that originate from their focus. Link prediction uses evidence to predict network structure evolution, whereas our focal point is directed communication initiation between
users who are previously not structurally connected. To address this problem, we employ topological evidence in conjunction to transactional information in order to predict communication intention. It is not intuitive whether methods that work well for
link prediction would work well in this case. In fact, we show in this work that network or content evidence, when considered separately, are not sufficiently accurate predictors. Our novel approach, which jointly considers local structural properties of users in a social network, in conjunction with their generated content, captures numerous interactions, direct and indirect, social and contextual, which have up to date been considered independently. We performed an empirical study to evaluate our method using an extracted network of directed @-messages sent between users of a corporate microblogging service, which resembles Twitter. We find that our method outperforms state of the art techniques for link prediction. Our findings have implications for a wide range of social web applications, such as contextual expert recommendation for Q&A, new friendship relationships creation, and targeted content delivery.
Hierarchical clustering methods create a hierarchy of clusters based on distance or similarity measures. They do not require specifying the number of clusters k in advance. Hierarchical methods either merge smaller clusters into larger ones (agglomerative) or split larger clusters into smaller ones (divisive) at each step. This continues recursively until all objects are linked or placed into individual clusters.
This document proposes fast single-pass k-means clustering algorithms to allow for fast nearest neighbor search on large datasets. It discusses the rationale for using k-means clustering, describes algorithms like ball k-means and surrogate methods that can perform clustering in a single pass. It covers implementations using techniques like locality sensitive hashing and projection search to speed up vector searches. Evaluation on synthetic and real datasets shows the algorithms can achieve the same or better accuracy as traditional k-means 10x faster, enabling applications like fast nearest neighbor search on massive datasets for applications like customer modeling.
This document discusses different types of knowledge and methods for knowledge acquisition. It describes declarative and procedural knowledge, as well as the knowledge acquisition paradox where experts have difficulty verbalizing their knowledge. Various knowledge acquisition methods are outlined, including observation, problem discussion, and protocol analysis. Knowledge representation techniques like rules, semantic networks, frames, and predicate logic are also introduced.
This document outlines a presentation on biological networks and the software Cytoscape. It begins with an introduction to biological networks and their taxonomy, as well as analytical approaches and visualization techniques. It then provides an overview of Cytoscape, covering core concepts like networks and tables, visual properties, and apps. The document demonstrates how to load networks and data, use visual style managers, and save and export networks. It concludes with tips and tricks for using Cytoscape and a link to a hands-on tutorial.
Keynote given at the workshop for Artificial Intelligence meets the Web of Data on Pragmatic Semantics.
In this keynote I argue that the Web of Data is a Complex System or Marketplace of Ideas rather than a classical Database, and that the model theory on which classical semantics are based is not appropriate in all situations, and propose an alternative "Pragmatic Semantics" based on optimisation of possible interpretations. .
Machine Learning from Statistical Point of ViewYury Gubman
Dr. Yury Gubman introduced himself as a statistician with experience in statistics, machine learning, and leading AI teams. He discussed how machine learning relates to statistics, with machine learning models representing statistical models estimated through different procedures. He emphasized using statistical theory to calculate additional predictive features, treat outliers, perform feature selection, and maintain models over time. He provided examples of applying these statistical approaches to crime prediction and housing price estimation problems.
In social networks, where users send messages to each other, the issue of what triggers communication between unrelated users arises: does communication between previously unrelated users depend on friend-of-a-friend type of relationships, common interests, or other factors? In this work, we study the problem of predicting directed communication
intention between two users. Link prediction is similar to communication intention in that it uses network structure for prediction. However, these two problems exhibit fundamental
differences that originate from their focus. Link prediction uses evidence to predict network structure evolution, whereas our focal point is directed communication initiation between
users who are previously not structurally connected. To address this problem, we employ topological evidence in conjunction to transactional information in order to predict communication intention. It is not intuitive whether methods that work well for
link prediction would work well in this case. In fact, we show in this work that network or content evidence, when considered separately, are not sufficiently accurate predictors. Our novel approach, which jointly considers local structural properties of users in a social network, in conjunction with their generated content, captures numerous interactions, direct and indirect, social and contextual, which have up to date been considered independently. We performed an empirical study to evaluate our method using an extracted network of directed @-messages sent between users of a corporate microblogging service, which resembles Twitter. We find that our method outperforms state of the art techniques for link prediction. Our findings have implications for a wide range of social web applications, such as contextual expert recommendation for Q&A, new friendship relationships creation, and targeted content delivery.
Hierarchical clustering methods create a hierarchy of clusters based on distance or similarity measures. They do not require specifying the number of clusters k in advance. Hierarchical methods either merge smaller clusters into larger ones (agglomerative) or split larger clusters into smaller ones (divisive) at each step. This continues recursively until all objects are linked or placed into individual clusters.
This document proposes fast single-pass k-means clustering algorithms to allow for fast nearest neighbor search on large datasets. It discusses the rationale for using k-means clustering, describes algorithms like ball k-means and surrogate methods that can perform clustering in a single pass. It covers implementations using techniques like locality sensitive hashing and projection search to speed up vector searches. Evaluation on synthetic and real datasets shows the algorithms can achieve the same or better accuracy as traditional k-means 10x faster, enabling applications like fast nearest neighbor search on massive datasets for applications like customer modeling.
EgoSystem: Presentation to LITA, American Library Association, Nov 8 2014James Powell
The Internet represents the connections among computers and devices, the world wide web is a network of interconnected documents, and the semantic web is the closest thing we have today to a network of interconnected facts. Noticeably absent from these global networks is any sort of open, formal representation for an online global social network. Each users' online presence, and its immediate social network, are isolated and typically only available within the confines of the social networking site that hosts it. Discovery across explicit online social networks and implicit social networks such as those that can be inferred from co-authorship relationships and affiliations is, for all practical purposes, impossible. And yet there are practical and non-nefarious reasons why an organization might be interested in exploring portions of such a network. Outreach is one such interest. Los Alamos National Laboratory (LANL) prototyped EgoSystem to harvest and explore the professional social networks of post doctoral students. The project's goal is to enlist past students and other Lab alumni as ambassadors and advocates for LANL's ongoing mission. During this talk we will discuss the various technologies that support the EgoSystem and demonstrate some of its capabilities.
The document discusses characteristics of the web graph and power law distributions. Some key points:
1. The web can be modeled as a graph with pages as nodes and hyperlinks as edges. Distributions of inlinks, outlinks, and site sizes often follow power laws.
2. Power law distributions have heavy tails where rare, high-value events are more likely than in normal distributions. Examples include Pareto and Zipf distributions.
3. Analyses of the web graph have found that indegree, outdegree, and other metrics like PageRank scores and site popularities often follow power laws.
4. The web graph exhibits self-similarity, where subgraphs and focused subsets also display power
A high-level overview of social network analysis using gephi with your exported Facebook friends network. See more network analysis at http://allthingsgraphed.com.
Summary: Graphs are structures commonly used in computer science that model the interactions among entities. I will start from introducing the basic formulations of graph based machine learning, which has been a popular topic of research in the past decade and led to a powerful set of techniques. Particularly, I will show examples on how it acts as a generic data mining and predictive analytic tool. In the second part, I am going to discuss applications of such learning techniques in media analytics: (1) image analysis, where visually coherent objects are isolated from images; (2) social analysis of videos, where actors' social properties are predicted from videos. Materials in this part are based on our recent publications in highly selective venues (papers on https://sites.google.com/site/leiding2010/ ).
Bio: Lei Ding is a researcher making sense of large amounts of data in all media types. He currently works in Intent Media as a scientist, focusing on data analytics and applied machine learning in online advertising. Previously, he has worked in several research institutions including Columbia University, UIUC and IBM Research on digital / social media analysis and understanding. He received a Ph.D. degree in Computer Science and Engineering from The Ohio State University, where he was a Distinguished University Fellow.
This document summarizes techniques for social network analysis using content and graphs. It discusses how to construct graphs from varied data sources by extracting entities, relationships, and representations from unstructured text. It then covers approaches to community detection in networks, including modularity optimization, Infomap, and spectral clustering algorithms. It provides an example analysis using the ISVG dataset of terrorist and criminal groups.
Network Visualization guest lecture at #DataVizQMSS at @Columbia / #SNA at PU...Denis Parra Santander
- First version was a guest lecture about Network Visualization in the class "Data Visualization" taught by Dr. Sharon Hsiao in the QMSS program at Columbia University http://www.columbia.edu/~ih2240/dataviz/index.htm
- This updated version was delivered in our class on SNA at PUC Chile in the MPGI master program.
Graphical models provide a framework for combining probabilities and logical structures to compactly represent complex phenomena. Bayesian networks are a type of graphical model that use directed graphs to represent conditional independence relationships between variables. Each node corresponds to a variable with a conditional probability distribution. This factorization allows compressing a full joint distribution into a smaller set of local distributions. Markov networks are another type of graphical model that use undirected graphs and factors to represent relationships without directionality. Graphical models have applications in areas like medical diagnosis, computer vision, and combining logic and probability. Tools exist to help construct and evaluate graphical models.
This document summarizes key concepts for describing networks, including centrality measures, connectivity, cohesion, and roles. It discusses measuring the importance of individual nodes through degrees, closeness, betweenness, and power centrality. It also covers sociocentric measures like degree distributions, centralization, and density. Additionally, it explores local connectivity through triads, transitivity, and clustering coefficients as well as structural cohesion through components and cut points.
High-performance graph analysis is unlocking knowledge in computer security, bioinformatics, social networks, and many other data integration areas. Graphs provide a convenient abstraction for many data problems beyond linear algebra. Some problems map directly to linear algebra. Others, like community detection, look eerily similar to sparse linear algebra techniques. And then there are algorithms that strongly resist attempts at making them look like linear algebra. This talk will cover recent results with an emphasis on streaming graph problems where the graph changes and results need updated with minimal latency. We’ll also touch on issues of sensitivity and reliability where graph analysis needs to learn from numerical analysis and linear algebra.
Natural Language Processing in R (rNLP)fridolin.wild
The introductory slides of a workshop given to the doctoral school at the Institute of Business Informatics of the Goethe University Frankfurt. The tutorials are available on http://crunch.kmi.open.ac.uk/w/index.php/Tutorials
- What is Clustering, Honeypots and Density Based Clustering?
- What is Optics Clustering and how is it different than DB Clustering? …and how
can it be used for outlier detection.
- What is so-called soft clustering and how is it different than clustering? …and how
can it be used for outlier detection.
Data Tactics Data Science Brown Bag (April 2014)Rich Heimann
This is a presentation we perform internally every quarter as part of our Data Science Brown Bag Series. This presentation was talking about different types of soft clustering techniques - all of which the team currently performs depending on the complexity of the data and the complexity of customer problems. If you are interested in learning more about working with L-3 Data Tactics or interested in working for the L-3 Data Tactics Data Science team please contact us soon! Thank you.
Social Network Analysis - Lecture 4 in Introduction to Computational Social S...Lauri Eloranta
Fourth lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
This document summarizes machine learning techniques used at NASA's Jet Propulsion Laboratory. It discusses how machine learning can be used to analyze large datasets that are too complex for humans to fully examine alone. Examples include identifying features in hyperspectral images and discovering patterns in genetic and meteorological data. Both supervised and unsupervised machine learning algorithms are covered.
This document discusses k-nearest neighbors (KNN) classification, an instance-based machine learning algorithm. KNN works by finding the k training examples closest in distance to a new data point, and assigning the most common class among those k neighbors as the prediction for the new point. The document notes that KNN has high variance, since each data point acts as its own hypothesis. It suggests ways to reduce overfitting, such as using KNN with multiple neighbors (k>1), weighting neighbors by distance, and approximating KNN with data structures like k-d trees.
This document describes the BLOOMS+ approach for performing contextual ontology alignment of Linked Open Data datasets with an upper ontology. It presents the challenges of existing ontology matching approaches when applied to large, diverse LOD datasets. BLOOMS+ leverages structured knowledge from Wikipedia categories to generate "BLOOMS trees" representing different senses of concept names, and calculates similarity between trees to identify subclass, equivalence and other relationships between concepts in different datasets. It outperforms existing approaches on aligning three real-world LOD ontologies to the PROTON upper ontology. Future work aims to improve the weighting and identify additional contextual sources to assist with schema alignment across datasets.
This chapter discusses clustering connections on LinkedIn based on job title to find similarities. It covers standardizing job titles, common similarity metrics like edit distance and Jaccard distance, and clustering algorithms like greedy clustering, hierarchical clustering and k-means clustering. It also discusses fetching extended profile information using OAuth authorization to access private LinkedIn data without credentials. The goal is to answer questions about connections by clustering them based on attributes like job title, company or location.
Michael Mitzenmacher proposes that power law research has progressed through 5 stages: observation, interpretation, modeling, validation, and control. While much work has focused on observation and modeling, the field would benefit from more emphasis on validation and control. Validation is important to determine the appropriate underlying model and allow extrapolation. Control could design ways to modify system behavior based on validated models. Collaboration between theory, systems research, statistics and other fields may provide insights to advance validation and control of power law systems.
This document discusses various techniques for data preprocessing, including data integration, transformation, reduction, and discretization. It covers topics such as schema integration, handling redundant data, data normalization, dimensionality reduction, data cube aggregation, sampling, and entropy-based discretization. The goal of these techniques is to prepare raw data for knowledge discovery and data mining tasks by cleaning, transforming, and reducing the data into a suitable structure.
Multi-Model Data Query Languages and Processing ParadigmsJiaheng Lu
Specifying users' interests with a formal query language is a typically challenging task, which becomes even harder in the context of multi-model data management because we have to deal with data variety. It usually lacks a unified schema to help the users issuing their queries, or has an incomplete schema as data come from disparate sources. Multi-Model DataBases (MMDBs) have emerged as a promising approach for dealing with this task as they are capable of accommodating and querying the multi-model data in a single system. This tutorial aims to offer a comprehensive presentation of a wide range of query languages for MMDBs and to make comparisons of their properties from multiple perspectives. We will discuss the essence of cross-model query processing and provide insights on the research challenges and directions for future work. The tutorial will also offer the participants hands-on experience in applying MMDBs to issue multi-model data queries.
This document discusses the key building blocks of algorithms, including problem solving techniques, the problem solving process, and common algorithm structures. It describes algorithms as step-by-step procedures to solve problems and introduces common algorithm structures like sequences, selections, and iterations. It also discusses the basic components of algorithms, such as statements, state, control flow, and functions. Functions are described as reusable blocks of code that perform specific tasks to simplify complex problems.
This document provides an overview of cryptography and network security. It defines key terms like computer security, network security, and internet security. It describes common security attacks like eavesdropping, tampering, fabrication, and denial of service. It also outlines security services, security mechanisms, and the OSI security architecture. The document models network security and network access security.
EgoSystem: Presentation to LITA, American Library Association, Nov 8 2014James Powell
The Internet represents the connections among computers and devices, the world wide web is a network of interconnected documents, and the semantic web is the closest thing we have today to a network of interconnected facts. Noticeably absent from these global networks is any sort of open, formal representation for an online global social network. Each users' online presence, and its immediate social network, are isolated and typically only available within the confines of the social networking site that hosts it. Discovery across explicit online social networks and implicit social networks such as those that can be inferred from co-authorship relationships and affiliations is, for all practical purposes, impossible. And yet there are practical and non-nefarious reasons why an organization might be interested in exploring portions of such a network. Outreach is one such interest. Los Alamos National Laboratory (LANL) prototyped EgoSystem to harvest and explore the professional social networks of post doctoral students. The project's goal is to enlist past students and other Lab alumni as ambassadors and advocates for LANL's ongoing mission. During this talk we will discuss the various technologies that support the EgoSystem and demonstrate some of its capabilities.
The document discusses characteristics of the web graph and power law distributions. Some key points:
1. The web can be modeled as a graph with pages as nodes and hyperlinks as edges. Distributions of inlinks, outlinks, and site sizes often follow power laws.
2. Power law distributions have heavy tails where rare, high-value events are more likely than in normal distributions. Examples include Pareto and Zipf distributions.
3. Analyses of the web graph have found that indegree, outdegree, and other metrics like PageRank scores and site popularities often follow power laws.
4. The web graph exhibits self-similarity, where subgraphs and focused subsets also display power
A high-level overview of social network analysis using gephi with your exported Facebook friends network. See more network analysis at http://allthingsgraphed.com.
Summary: Graphs are structures commonly used in computer science that model the interactions among entities. I will start from introducing the basic formulations of graph based machine learning, which has been a popular topic of research in the past decade and led to a powerful set of techniques. Particularly, I will show examples on how it acts as a generic data mining and predictive analytic tool. In the second part, I am going to discuss applications of such learning techniques in media analytics: (1) image analysis, where visually coherent objects are isolated from images; (2) social analysis of videos, where actors' social properties are predicted from videos. Materials in this part are based on our recent publications in highly selective venues (papers on https://sites.google.com/site/leiding2010/ ).
Bio: Lei Ding is a researcher making sense of large amounts of data in all media types. He currently works in Intent Media as a scientist, focusing on data analytics and applied machine learning in online advertising. Previously, he has worked in several research institutions including Columbia University, UIUC and IBM Research on digital / social media analysis and understanding. He received a Ph.D. degree in Computer Science and Engineering from The Ohio State University, where he was a Distinguished University Fellow.
This document summarizes techniques for social network analysis using content and graphs. It discusses how to construct graphs from varied data sources by extracting entities, relationships, and representations from unstructured text. It then covers approaches to community detection in networks, including modularity optimization, Infomap, and spectral clustering algorithms. It provides an example analysis using the ISVG dataset of terrorist and criminal groups.
Network Visualization guest lecture at #DataVizQMSS at @Columbia / #SNA at PU...Denis Parra Santander
- First version was a guest lecture about Network Visualization in the class "Data Visualization" taught by Dr. Sharon Hsiao in the QMSS program at Columbia University http://www.columbia.edu/~ih2240/dataviz/index.htm
- This updated version was delivered in our class on SNA at PUC Chile in the MPGI master program.
Graphical models provide a framework for combining probabilities and logical structures to compactly represent complex phenomena. Bayesian networks are a type of graphical model that use directed graphs to represent conditional independence relationships between variables. Each node corresponds to a variable with a conditional probability distribution. This factorization allows compressing a full joint distribution into a smaller set of local distributions. Markov networks are another type of graphical model that use undirected graphs and factors to represent relationships without directionality. Graphical models have applications in areas like medical diagnosis, computer vision, and combining logic and probability. Tools exist to help construct and evaluate graphical models.
This document summarizes key concepts for describing networks, including centrality measures, connectivity, cohesion, and roles. It discusses measuring the importance of individual nodes through degrees, closeness, betweenness, and power centrality. It also covers sociocentric measures like degree distributions, centralization, and density. Additionally, it explores local connectivity through triads, transitivity, and clustering coefficients as well as structural cohesion through components and cut points.
High-performance graph analysis is unlocking knowledge in computer security, bioinformatics, social networks, and many other data integration areas. Graphs provide a convenient abstraction for many data problems beyond linear algebra. Some problems map directly to linear algebra. Others, like community detection, look eerily similar to sparse linear algebra techniques. And then there are algorithms that strongly resist attempts at making them look like linear algebra. This talk will cover recent results with an emphasis on streaming graph problems where the graph changes and results need updated with minimal latency. We’ll also touch on issues of sensitivity and reliability where graph analysis needs to learn from numerical analysis and linear algebra.
Natural Language Processing in R (rNLP)fridolin.wild
The introductory slides of a workshop given to the doctoral school at the Institute of Business Informatics of the Goethe University Frankfurt. The tutorials are available on http://crunch.kmi.open.ac.uk/w/index.php/Tutorials
- What is Clustering, Honeypots and Density Based Clustering?
- What is Optics Clustering and how is it different than DB Clustering? …and how
can it be used for outlier detection.
- What is so-called soft clustering and how is it different than clustering? …and how
can it be used for outlier detection.
Data Tactics Data Science Brown Bag (April 2014)Rich Heimann
This is a presentation we perform internally every quarter as part of our Data Science Brown Bag Series. This presentation was talking about different types of soft clustering techniques - all of which the team currently performs depending on the complexity of the data and the complexity of customer problems. If you are interested in learning more about working with L-3 Data Tactics or interested in working for the L-3 Data Tactics Data Science team please contact us soon! Thank you.
Social Network Analysis - Lecture 4 in Introduction to Computational Social S...Lauri Eloranta
Fourth lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
This document summarizes machine learning techniques used at NASA's Jet Propulsion Laboratory. It discusses how machine learning can be used to analyze large datasets that are too complex for humans to fully examine alone. Examples include identifying features in hyperspectral images and discovering patterns in genetic and meteorological data. Both supervised and unsupervised machine learning algorithms are covered.
This document discusses k-nearest neighbors (KNN) classification, an instance-based machine learning algorithm. KNN works by finding the k training examples closest in distance to a new data point, and assigning the most common class among those k neighbors as the prediction for the new point. The document notes that KNN has high variance, since each data point acts as its own hypothesis. It suggests ways to reduce overfitting, such as using KNN with multiple neighbors (k>1), weighting neighbors by distance, and approximating KNN with data structures like k-d trees.
This document describes the BLOOMS+ approach for performing contextual ontology alignment of Linked Open Data datasets with an upper ontology. It presents the challenges of existing ontology matching approaches when applied to large, diverse LOD datasets. BLOOMS+ leverages structured knowledge from Wikipedia categories to generate "BLOOMS trees" representing different senses of concept names, and calculates similarity between trees to identify subclass, equivalence and other relationships between concepts in different datasets. It outperforms existing approaches on aligning three real-world LOD ontologies to the PROTON upper ontology. Future work aims to improve the weighting and identify additional contextual sources to assist with schema alignment across datasets.
This chapter discusses clustering connections on LinkedIn based on job title to find similarities. It covers standardizing job titles, common similarity metrics like edit distance and Jaccard distance, and clustering algorithms like greedy clustering, hierarchical clustering and k-means clustering. It also discusses fetching extended profile information using OAuth authorization to access private LinkedIn data without credentials. The goal is to answer questions about connections by clustering them based on attributes like job title, company or location.
Michael Mitzenmacher proposes that power law research has progressed through 5 stages: observation, interpretation, modeling, validation, and control. While much work has focused on observation and modeling, the field would benefit from more emphasis on validation and control. Validation is important to determine the appropriate underlying model and allow extrapolation. Control could design ways to modify system behavior based on validated models. Collaboration between theory, systems research, statistics and other fields may provide insights to advance validation and control of power law systems.
This document discusses various techniques for data preprocessing, including data integration, transformation, reduction, and discretization. It covers topics such as schema integration, handling redundant data, data normalization, dimensionality reduction, data cube aggregation, sampling, and entropy-based discretization. The goal of these techniques is to prepare raw data for knowledge discovery and data mining tasks by cleaning, transforming, and reducing the data into a suitable structure.
Multi-Model Data Query Languages and Processing ParadigmsJiaheng Lu
Specifying users' interests with a formal query language is a typically challenging task, which becomes even harder in the context of multi-model data management because we have to deal with data variety. It usually lacks a unified schema to help the users issuing their queries, or has an incomplete schema as data come from disparate sources. Multi-Model DataBases (MMDBs) have emerged as a promising approach for dealing with this task as they are capable of accommodating and querying the multi-model data in a single system. This tutorial aims to offer a comprehensive presentation of a wide range of query languages for MMDBs and to make comparisons of their properties from multiple perspectives. We will discuss the essence of cross-model query processing and provide insights on the research challenges and directions for future work. The tutorial will also offer the participants hands-on experience in applying MMDBs to issue multi-model data queries.
This document discusses the key building blocks of algorithms, including problem solving techniques, the problem solving process, and common algorithm structures. It describes algorithms as step-by-step procedures to solve problems and introduces common algorithm structures like sequences, selections, and iterations. It also discusses the basic components of algorithms, such as statements, state, control flow, and functions. Functions are described as reusable blocks of code that perform specific tasks to simplify complex problems.
This document provides an overview of cryptography and network security. It defines key terms like computer security, network security, and internet security. It describes common security attacks like eavesdropping, tampering, fabrication, and denial of service. It also outlines security services, security mechanisms, and the OSI security architecture. The document models network security and network access security.
This document provides information about the phases and objectives of a compiler design course. It discusses the following key points:
- The course aims to teach students about the various phases of a compiler like parsing, code generation, and optimization techniques.
- The outcomes include explaining the compilation process and building tools like lexical analyzers and parsers. Students should also be able to develop semantic analysis and code generators.
- The document then covers the different phases of a compiler in detail, including lexical analysis, syntax analysis, semantic analysis, intermediate code generation, and code optimization. It provides examples to illustrate each phase.
Exceptions in Python represent errors and unexpected events that occur during program execution. There are several ways to handle exceptions in Python code:
1. Try and except blocks allow catching specific exceptions, with except blocks handling the exception.
2. Multiple except blocks can handle different exception types. The else block runs if no exception occurs.
3. Exceptions can be raised manually with raise or instantiated before raising. Finally blocks ensure code runs regardless of exceptions.
This chapter discusses the rise of big data due to increases in data processing capabilities, data storage, and communication technologies. It defines big data as extremely large data sets that are difficult to process using traditional methods. Big data comes from a variety of sources and requires real-time analysis. It also outlines the key players in the big data value chain including data collectors, aggregators, and users.
Recursion is defined as a technique where a function calls itself, either directly or indirectly. It involves breaking a problem down into smaller sub-problems until it reaches a base case. For recursion to work properly, it needs a base case where the problem can be solved without further recursion, and each recursive call must make progress towards the base case. While recursion can provide elegant solutions, it uses more memory and time than iterative approaches like loops. Common pitfalls are not having a base case, not making progress on each call, or using too many resources on each recursive call.
The document discusses data management techniques for social network analysis. It covers how to format network data for import into analysis software, how to transform data to make it suitable for different analyses, and how to export data and results. Specific transformation techniques discussed include transposing matrices, imputing missing values, symmetrizing and dichotomizing networks, combining multiple relations, combining nodes, and extracting subgraphs. Proper data management is presented as an important first step for network analysis.
Network science is an interdisciplinary field that studies complex networks. It draws on theories from mathematics, physics, computer science, statistics, and sociology. The document provides an introduction to network science and outlines topics including network analysis, visualization, and business applications. It also summarizes the history and development of network science as an academic field.
A computer is a machine that can accept data as input, process the data, and provide results as output. It works by executing stored programs using a central processing unit (CPU). Programs are written in low-level machine code or assembly language, which are then compiled into machine code. Computers use binary numbers and digital encoding to represent all data internally.
The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
13. Describing Networks
• Geodesic
– shortest_path(n,m)
• Diameter
– max(geodesic(n,m)) n,m actors in graph
• Density
– Number of existing edges / All possible edges
• Degree distribution
14. Types of Networks/Models
• A few quick examples
– Erdős–Rényi
• G(n,M): randomly draw M edges between n nodes
• Does not really model the real world
– Average connectivity on nodes conserved
15. Types of Networks/Models
• A few quick examples
– Erdős–Rényi
– Small World
• Watts-Strogatz
• Kleinberg lattice model
16. NE
MA
Milgram’s experiment (1960’s):
Given a target individual and a particular property, pass the message to a person
you correspond with who is “closest” to the target.
Small world experiments then
17. Watts-Strogatz Ring Lattice Rewiring
• As in many network generating algorithms
• Disallow self-edges
• Disallow multiple edges
Select a fraction p of
edges
Reposition on of their
endpoints
Add a fraction p of
additional edges leaving
underlying lattice intact
19. Kleinberg Lattice Model
nodes are placed on a lattice and
connect to nearest neighbors
additional links placed with puv ~ r
uv
d
Kleinberg, ‘The Small World Phenomenon, An Algorithmic Perspective’
(Nature 2000)
20.
21. A little more on degree distribution
• Power-laws, zipf, etc.
Distribution of users among
web sites
CDF of users to sites
Sites ranked by popularity
22. A little more on degree distribution
• Pareto/Power-law
– Pareto: CDF P[X > x] ~ x-k
– Power-law: PDF P[X = x] ~ x-(k+1) = x-a
– Some recent debate (Aaron Clauset)
• http://arxiv.org/abs/0706.1062
• Zipf
– Frequency versus rank y ~ r-b (small b)
• More info:
– Zipf, Power-laws, and Pareto – a ranking tutorial
(http://www.hpl.hp.com/research/idl/papers/ranking
/ranking.html)
23. Types of Networks/Models
• A few quick examples
– Erdős–Rényi
– Small World
• Watts-Strogatz
• Kleinberg lattice model
– Preferential Attachment
• Generally attributed to Barabási & Albert
24. Basic BA-model
• Very simple algorithm to implement
– start with an initial set of m0 fully connected nodes
• e.g. m0 = 3
– now add new vertices one by one, each one with exactly m
edges
– each new edge connects to an existing vertex in proportion
to the number of edges that vertex already has →
preferential attachment
25. Properties of the BA graph
• The distribution is scale free with exponent a = 3
P(k) = 2 m2/k3
• The graph is connected
– Every new vertex is born with a link or several links
(depending on whether m = 1 or m > 1)
– It then connects to an ‘older’ vertex, which itself
connected to another vertex when it was introduced
– And we started from a connected core
• The older are richer
– Nodes accumulate links as time goes on, which gives older
nodes an advantage since newer nodes are going to attach
preferentially – and older nodes have a higher degree to
tempt them with than some new kid on the block
33. Centrality Measures
• Degree centrality
– Edges per node (the more, the more important the node)
• Closeness centrality
– How close the node is to every other node
• Betweenness centrality
– How many shortest paths go through the edge node
(communication metaphor)
• Information centrality
– All paths to other nodes weighted by path length
• Bibliometric + Internet style
– PageRank
34. Tie Strength
• Strength of Weak Ties (Granovetter)
– Granovetter: How often did you see the contact that helped you find the job prior
to the job search
• 16.7 % often (at least once a week)
• 55.6% occasionally (more than once a year but less than twice a week)
• 27.8% rarely – once a year or less
– Weak ties will tend to have different information than we and our close contacts
do
weak ties will tend to have high
beweenness and low transitivity
37. Link Prediction in Social Net Data
• We know things about structure
– Homophily = like likes like or bird of a feather flock
together or similar people group together
– Mutuality
– Triad closure
• Various measures that try to use this
38. Link Prediction
• Simple metrics
– Only take into
account graph
properties
Liben-Nowell, Kleinberg (CIKM’03)
( ) ( )
1
log | ( ) |
z x y z
Γ(x) = neighbors of x
Originally: 1 / log(frequency(z))
39. Link Prediction
• Simple metrics
– Only take into
account graph
properties
Liben-Nowell, Kleinberg (CIKM’03)
,
1
| |
l l
x y
l
paths
Paths of length l (generally 1)
from x to y
weighted variant is the number of
times the two collaborated
40. Link Prediction in Relational Data
• We know things about structure
– Homophily = like likes like or bird of a feather flock
together or similar people group together
– Mutuality
– Triad closure
• Slightly more interesting problem if we have
relational data on actors and ties
– Move beyond structure
41. Relationship & Link Prediction
advisorOf?
Employee /contractor
Salary
Time at company
…
42. Link/Label Prediction in Relational Data
• Koller and co.
– Relational Bayesian Networks
– Relational Markov Networks
• Structure (subgraph templates/cliques)
– Similar context
– Transitivity
• Getoor and co.
– Relationship Identification for Social Network Discovery
• Diehl/Namata/Getoor AAAI’07
– Enron data
• Traffic statistics and content to find supervisory relationships?
– Traffic/Text based
– Not really identification, more like ranking
44. Epidemiological
• Viruses
– Biological, computational
– STDs, needle sharing, etc.
– Mark Handcock at UW
• Blog networks
– Applying SIR models (Info Diffusion Through Blogspace, Gruhl et
al.)
• Induce transmission graph, cascade models, simulation
– Link prediction (Tracking Information Epidemics in Blogspace,
Adar et al.)
• Find repeated “likely” infections
– Outbreak detection (Cost-effective Outbreak Detection in
Networks, Leskovec et al.)
• Submodularity
49. Blockmodels
• Actors are portioned into positions
– Rearrange rows/columns
• The sociomatrix is then reduced to a smaller
image
• Hierarchical clustering
– Various distance metrics
• Euclidean, CONvergence of CORrelation (CONCOR)
• Various “fit” metrics
56. Network motif detection
• How many more motifs of a certain type exist
over a random network
• Started in biological networks
– http://www.weizmann.ac.il/mcb/UriAlon/
57. Basic idea
• construct many random graphs with the same
number of nodes and edges (same node
degree distribution?)
• count the number of motifs in those graphs
• calculate the Z score: the probability that the
given number of motifs in the real world
network could have occurred by chance
58.
59. Generating random graphs
• Many models don’t preserve the desired
features
• Have to be careful how we generate
62. Privacy
• Emerging interest in anonymizing networks
– Lars Backstrom (WWW’07) demonstrated one of
the first attacks
• How to remove labels while preserving graph
properties?
– While ensuring that labels cannot be reapplied
65. Books/Journals/Conferences
• Social Networks/Phs. Rev
• Social Network Analysis (Wasserman + Faust)
• The Development of Social Network Analysis
(Freeman)
• Linked (Barabsi)
• Six Degrees (Watts)
• Sunbelt/ICWSM/KDD/CIKM/NIPS
67. Assortativity
• Social networks are assortative:
– the gregarious people associate with other gregarious people
– the loners associate with other loners
• The Internet is disassorative:
Assortative:
hubs connect to hubs
Random
Disassortative:
hubs are in the periphery