SlideShare uma empresa Scribd logo
1 de 25
Network analysis basic
• in-degree: how many directed edges (arcs)
are incident on a node
• out-degree: how may directed edges
originate at a node
• Degree sequence: [4, 4, 3, 7, …]
Node properties
Generated properties of node
• Clustering coefficient: how your neighbors
connected together
• ego-density: density of the surrounding net
UCINET: Network>Ego-networks>egonet basic measures
• Directed or undirected
• Weight, ranking, ...
• Type, negative or positive, ...
• Assigned-properties depending on
calculating network itself, e.g., betweenness
Edge properties
Network Properties
• Degree distribution: Frequency of degree
sequences
• Size: number of nodes (n)
• Density: real relations divided by the maximum
possible relations
• Diameter: the length of the longest path
• Average degree of separation:Average length of all
possible shorted path
UCINET: Network>Cohesion>Density
Mode of network
• One-mode network
‣ Friendship
‣ Collaboration e.g., User-paper represented by 2-mode network
• Two-mode network—bipartite network
‣ User-borrowed book, co-bought
‣ Affiliation network— e.g., Member-Guild, Employee-Company
Resolved by co-occurrence-ship
Network analysis basic
• Degree
How many resource do you have?
• Closeness
How far apart are you from others?
• Betweenness
How important are you for bridging
sub-communities?
• Centralization
How balanced are actors’ centrality?
Centrality
Individual
level
Global
level
• Density
How does the network tied together?
• Separation, Diameter
How far apart are you and your friends?
• Cluster Coefficient
How do your neighbors be connected?
Individual
level
Global
level
Visualization through analysis process
1. Take a look
2. Analyze and find significant features such as sub-
components or special positions
3. Draw the network according to the result of
analysis
4. Color by the node features (e.g., sex, position, ...)
and create hypothesis
5. Verify the hypothesis
• Ego-network Analysis
-
-
• Partial Network Analysis
- One, two or three steps network two steps network
- Boundary or sub-cluster of network
• Whole Network Analysis
- /
Motif
-
Levels of network analysis
• Data is recorded with a clear natural-occurring
boundary and nodes in a boundary form a finite
set.
• What should be a possible boundary?
‣ A fixed location or room, specified time or day, a finite contact
tracing, a formal group in an organization, a family.
‣ The boundary is known or decided firstly, a priori, to be a
network.
Policy of recording data
• No sampling and tend to include all of the actors
in some population(s).
• Because network methods focus on relations
among actors, actors cannot be sampled
independently to be included as observations.
Policy of recording data (2)
• positivity A Priori
metaphysics
-
• -
• -
-
Butts, Carter T. "Revisiting the foundations of network analysis." Science325.5939 (2009): 414-416.
• Closed Complete
- finite
set
-
• Singularity
-
• Consistency
-
Butts, Carter T. "Revisiting the foundations of network analysis." Science325.5939 (2009): 414-416.
• Different relation sampling policy will cause
different results—Threshold effects on network
properties
• Threshold
Threshold
• Threshold 0
Connectedness
Betweenness
• Threshold Betweenness
Degree Betweenness
• facebook
10
Application
• Full network data is necessary to properly define and measure
many of the structural concepts of network analysis (e.g.
between-ness), however, very expensive.
• Snowball methods begin with a focal actor or set of actors until
no new actors are identified, or until we decide to stop.
- Useful to track down “special” population such as business contact networks, community
elites, deviant sub-cultures, avid stamp collectors, and kinship networks.
- The snowball method may tend to overstate the "connectedness" and "solidarity" of
populations of actors.
- There is no guaranteed way of finding all of the connected individuals in the population.
- How to select the first node (initial problem of sampling)?
- Incomplete problem of the snowball methods can be solved by use of multiple initial nodes.
Methods of sampling ties
Visualization
X Crossed-edges
X Uninformed-edge
length
X Overlapped
nodes and edges
4.1 network analysis basic
4.1 network analysis basic
4.1 network analysis basic
A B C D E F G H I J
A 0 1 1 1 0 1 0 0 0 0
B 1 0 0 1 1 0 1 0 0 0
C 1 0 0 1 0 1 0 0 0 0
D 1 1 1 0 1 1 1 0 0 0
E 0 1 0 1 0 0 1 0 0 0
F 1 0 1 1 0 0 1 1 0 0
G 0 1 0 1 1 1 0 1 0 0
H 0 0 0 0 0 1 1 0 1 0
I 0 0 0 0 0 0 0 1 0 1
J 0 0 0 0 0 0 0 0 1 0
Adjacent matrix
degree of B
Symmetric
M(1,4)=1, M(1,5)=0
Homans(1951) Metrics representation and manipulation
A B C D E F G H
A 1 1 1 1 1
B 1 1 1
C 1 1 1 1
D 1 1 1
E 1 1 1
F 1 1 1
G 1 1 1
H 1 1 1 1
D E C H A B F G
D 1 1 1
E 1 1 1
C 1 1 1 1
H 1 1 1 1
A 1 1 1 1 1
B 1 1 1
F 1 1 1
G 1 1 1

Mais conteúdo relacionado

Destaque

Cómo incorporar Periscope y Snapchat a tu estrategia digital: Webinar
Cómo incorporar Periscope y Snapchat a tu estrategia digital: WebinarCómo incorporar Periscope y Snapchat a tu estrategia digital: Webinar
Cómo incorporar Periscope y Snapchat a tu estrategia digital: WebinarIEBSchool
 
Microorganism By Azka Cantik
Microorganism By Azka CantikMicroorganism By Azka Cantik
Microorganism By Azka Cantikazkajoyo
 
Medios Sociales con Sentido
Medios Sociales con SentidoMedios Sociales con Sentido
Medios Sociales con SentidoCarlos Jiménez
 
Genba OR Gemba- A Problem Solving Technique
Genba OR Gemba- A Problem Solving TechniqueGenba OR Gemba- A Problem Solving Technique
Genba OR Gemba- A Problem Solving TechniqueSyed Anas Abdali
 
XING - Braucht man das oder kann das weg ...? #barcampDUS
XING - Braucht man das oder kann das weg ...? #barcampDUSXING - Braucht man das oder kann das weg ...? #barcampDUS
XING - Braucht man das oder kann das weg ...? #barcampDUSHolger Gottesmann
 
UR Sponsorship Proposal 2012
UR Sponsorship Proposal 2012 UR Sponsorship Proposal 2012
UR Sponsorship Proposal 2012 Stuart Meachem
 
NASCAR Sponsorship Package
NASCAR Sponsorship PackageNASCAR Sponsorship Package
NASCAR Sponsorship PackageZEALstreet
 
searchmetrics-webcast-importance-backlinks
searchmetrics-webcast-importance-backlinkssearchmetrics-webcast-importance-backlinks
searchmetrics-webcast-importance-backlinksJulia Schoenegger
 

Destaque (10)

Cómo incorporar Periscope y Snapchat a tu estrategia digital: Webinar
Cómo incorporar Periscope y Snapchat a tu estrategia digital: WebinarCómo incorporar Periscope y Snapchat a tu estrategia digital: Webinar
Cómo incorporar Periscope y Snapchat a tu estrategia digital: Webinar
 
Microorganism By Azka Cantik
Microorganism By Azka CantikMicroorganism By Azka Cantik
Microorganism By Azka Cantik
 
Abc costing
Abc costingAbc costing
Abc costing
 
Medios Sociales con Sentido
Medios Sociales con SentidoMedios Sociales con Sentido
Medios Sociales con Sentido
 
Team buraq racing car
Team buraq racing carTeam buraq racing car
Team buraq racing car
 
Genba OR Gemba- A Problem Solving Technique
Genba OR Gemba- A Problem Solving TechniqueGenba OR Gemba- A Problem Solving Technique
Genba OR Gemba- A Problem Solving Technique
 
XING - Braucht man das oder kann das weg ...? #barcampDUS
XING - Braucht man das oder kann das weg ...? #barcampDUSXING - Braucht man das oder kann das weg ...? #barcampDUS
XING - Braucht man das oder kann das weg ...? #barcampDUS
 
UR Sponsorship Proposal 2012
UR Sponsorship Proposal 2012 UR Sponsorship Proposal 2012
UR Sponsorship Proposal 2012
 
NASCAR Sponsorship Package
NASCAR Sponsorship PackageNASCAR Sponsorship Package
NASCAR Sponsorship Package
 
searchmetrics-webcast-importance-backlinks
searchmetrics-webcast-importance-backlinkssearchmetrics-webcast-importance-backlinks
searchmetrics-webcast-importance-backlinks
 

Semelhante a 4.1 network analysis basic

Network sampling, community detection
Network sampling, community detectionNetwork sampling, community detection
Network sampling, community detectionroberval mariano
 
network mining and representation learning
network mining and representation learningnetwork mining and representation learning
network mining and representation learningsun peiyuan
 
Social network analysis basics
Social network analysis basicsSocial network analysis basics
Social network analysis basicsPradeep Kumar
 
2016 Cytoscape 3.3 Tutorial
2016 Cytoscape 3.3 Tutorial2016 Cytoscape 3.3 Tutorial
2016 Cytoscape 3.3 TutorialAlexander Pico
 
02 Network Data Collection
02 Network Data Collection02 Network Data Collection
02 Network Data Collectiondnac
 
Graph Analysis Beyond Linear Algebra
Graph Analysis Beyond Linear AlgebraGraph Analysis Beyond Linear Algebra
Graph Analysis Beyond Linear AlgebraJason Riedy
 
CS6010 Social Network Analysis Unit V
CS6010 Social Network Analysis Unit VCS6010 Social Network Analysis Unit V
CS6010 Social Network Analysis Unit Vpkaviya
 
Practice discovering biological knowledge using networks approach.
Practice discovering biological knowledge using networks approach.Practice discovering biological knowledge using networks approach.
Practice discovering biological knowledge using networks approach.Elena Sügis
 
Efficient Reduced BIAS Genetic Algorithm for Generic Community Detection Obje...
Efficient Reduced BIAS Genetic Algorithm for Generic Community Detection Obje...Efficient Reduced BIAS Genetic Algorithm for Generic Community Detection Obje...
Efficient Reduced BIAS Genetic Algorithm for Generic Community Detection Obje...Aditya K G
 
Higher-order Link Prediction GraphEx
Higher-order Link Prediction GraphExHigher-order Link Prediction GraphEx
Higher-order Link Prediction GraphExAustin Benson
 
DS9 - Clustering.pptx
DS9 - Clustering.pptxDS9 - Clustering.pptx
DS9 - Clustering.pptxJK970901
 
Anomaly detection (Unsupervised Learning) in Machine Learning
Anomaly detection (Unsupervised Learning) in Machine LearningAnomaly detection (Unsupervised Learning) in Machine Learning
Anomaly detection (Unsupervised Learning) in Machine LearningKuppusamy P
 
Group and Community Detection in Social Networks
Group and Community Detection in Social NetworksGroup and Community Detection in Social Networks
Group and Community Detection in Social NetworksKent State University
 
3b318431-df9f-4a2c-9909-61ecb6af8444.pptx
3b318431-df9f-4a2c-9909-61ecb6af8444.pptx3b318431-df9f-4a2c-9909-61ecb6af8444.pptx
3b318431-df9f-4a2c-9909-61ecb6af8444.pptxNANDHINIS900805
 
Social network analysis
Social network analysisSocial network analysis
Social network analysisCaleb Jones
 

Semelhante a 4.1 network analysis basic (20)

Network sampling, community detection
Network sampling, community detectionNetwork sampling, community detection
Network sampling, community detection
 
network mining and representation learning
network mining and representation learningnetwork mining and representation learning
network mining and representation learning
 
Social network analysis basics
Social network analysis basicsSocial network analysis basics
Social network analysis basics
 
2016 Cytoscape 3.3 Tutorial
2016 Cytoscape 3.3 Tutorial2016 Cytoscape 3.3 Tutorial
2016 Cytoscape 3.3 Tutorial
 
02 Network Data Collection
02 Network Data Collection02 Network Data Collection
02 Network Data Collection
 
02 Network Data Collection (2016)
02 Network Data Collection (2016)02 Network Data Collection (2016)
02 Network Data Collection (2016)
 
05 Whole Network Descriptive Stats
05 Whole Network Descriptive Stats05 Whole Network Descriptive Stats
05 Whole Network Descriptive Stats
 
Graph Analysis Beyond Linear Algebra
Graph Analysis Beyond Linear AlgebraGraph Analysis Beyond Linear Algebra
Graph Analysis Beyond Linear Algebra
 
CS6010 Social Network Analysis Unit V
CS6010 Social Network Analysis Unit VCS6010 Social Network Analysis Unit V
CS6010 Social Network Analysis Unit V
 
Practice discovering biological knowledge using networks approach.
Practice discovering biological knowledge using networks approach.Practice discovering biological knowledge using networks approach.
Practice discovering biological knowledge using networks approach.
 
Efficient Reduced BIAS Genetic Algorithm for Generic Community Detection Obje...
Efficient Reduced BIAS Genetic Algorithm for Generic Community Detection Obje...Efficient Reduced BIAS Genetic Algorithm for Generic Community Detection Obje...
Efficient Reduced BIAS Genetic Algorithm for Generic Community Detection Obje...
 
Higher-order Link Prediction GraphEx
Higher-order Link Prediction GraphExHigher-order Link Prediction GraphEx
Higher-order Link Prediction GraphEx
 
DS9 - Clustering.pptx
DS9 - Clustering.pptxDS9 - Clustering.pptx
DS9 - Clustering.pptx
 
Anomaly detection (Unsupervised Learning) in Machine Learning
Anomaly detection (Unsupervised Learning) in Machine LearningAnomaly detection (Unsupervised Learning) in Machine Learning
Anomaly detection (Unsupervised Learning) in Machine Learning
 
02 Descriptive Statistics (2017)
02 Descriptive Statistics (2017)02 Descriptive Statistics (2017)
02 Descriptive Statistics (2017)
 
Group and Community Detection in Social Networks
Group and Community Detection in Social NetworksGroup and Community Detection in Social Networks
Group and Community Detection in Social Networks
 
3b318431-df9f-4a2c-9909-61ecb6af8444.pptx
3b318431-df9f-4a2c-9909-61ecb6af8444.pptx3b318431-df9f-4a2c-9909-61ecb6af8444.pptx
3b318431-df9f-4a2c-9909-61ecb6af8444.pptx
 
Pathway and network analysis
Pathway and network analysisPathway and network analysis
Pathway and network analysis
 
Social network analysis
Social network analysisSocial network analysis
Social network analysis
 
13047926.ppt
13047926.ppt13047926.ppt
13047926.ppt
 

Mais de jilung hsieh

"Indesign + folio - first class" for ntnu library workshop
"Indesign + folio - first class" for ntnu library workshop"Indesign + folio - first class" for ntnu library workshop
"Indesign + folio - first class" for ntnu library workshopjilung hsieh
 
"Introduction to making ebook" for ntnu library workshop
"Introduction to making ebook" for ntnu library workshop"Introduction to making ebook" for ntnu library workshop
"Introduction to making ebook" for ntnu library workshopjilung hsieh
 
e-magazine design by InDesign+folio - Multiple images
e-magazine design by InDesign+folio - Multiple imagese-magazine design by InDesign+folio - Multiple images
e-magazine design by InDesign+folio - Multiple imagesjilung hsieh
 
4.0 social network analysis
4.0 social network analysis4.0 social network analysis
4.0 social network analysisjilung hsieh
 
3.social network sites
3.social network sites3.social network sites
3.social network sitesjilung hsieh
 
2.social recommedation
2.social recommedation2.social recommedation
2.social recommedationjilung hsieh
 
0.introduction to social computing
0.introduction to social computing0.introduction to social computing
0.introduction to social computingjilung hsieh
 
Thinking in presentation
Thinking in presentationThinking in presentation
Thinking in presentationjilung hsieh
 
Tag system 教學投影片
Tag system 教學投影片Tag system 教學投影片
Tag system 教學投影片jilung hsieh
 
師大圖資研究生論文
師大圖資研究生論文師大圖資研究生論文
師大圖資研究生論文jilung hsieh
 
如何定義你的題目.Pptx
如何定義你的題目.Pptx如何定義你的題目.Pptx
如何定義你的題目.Pptxjilung hsieh
 
碩士論文寫作要點.Ppt
碩士論文寫作要點.Ppt碩士論文寫作要點.Ppt
碩士論文寫作要點.Pptjilung hsieh
 
如何定義你的題目.Pptx
如何定義你的題目.Pptx如何定義你的題目.Pptx
如何定義你的題目.Pptxjilung hsieh
 
碩士論文寫作要點.Ppt
碩士論文寫作要點.Ppt碩士論文寫作要點.Ppt
碩士論文寫作要點.Pptjilung hsieh
 
Reviewing abstract
Reviewing abstractReviewing abstract
Reviewing abstractjilung hsieh
 
Scott 校外口試
Scott 校外口試Scott 校外口試
Scott 校外口試jilung hsieh
 
Scott Complex Networks
Scott Complex NetworksScott Complex Networks
Scott Complex Networksjilung hsieh
 

Mais de jilung hsieh (18)

"Indesign + folio - first class" for ntnu library workshop
"Indesign + folio - first class" for ntnu library workshop"Indesign + folio - first class" for ntnu library workshop
"Indesign + folio - first class" for ntnu library workshop
 
"Introduction to making ebook" for ntnu library workshop
"Introduction to making ebook" for ntnu library workshop"Introduction to making ebook" for ntnu library workshop
"Introduction to making ebook" for ntnu library workshop
 
e-magazine design by InDesign+folio - Multiple images
e-magazine design by InDesign+folio - Multiple imagese-magazine design by InDesign+folio - Multiple images
e-magazine design by InDesign+folio - Multiple images
 
4.0 social network analysis
4.0 social network analysis4.0 social network analysis
4.0 social network analysis
 
3.social network sites
3.social network sites3.social network sites
3.social network sites
 
2.social recommedation
2.social recommedation2.social recommedation
2.social recommedation
 
0.introduction to social computing
0.introduction to social computing0.introduction to social computing
0.introduction to social computing
 
Thinking in presentation
Thinking in presentationThinking in presentation
Thinking in presentation
 
Tag system 教學投影片
Tag system 教學投影片Tag system 教學投影片
Tag system 教學投影片
 
師大圖資研究生論文
師大圖資研究生論文師大圖資研究生論文
師大圖資研究生論文
 
如何定義你的題目.Pptx
如何定義你的題目.Pptx如何定義你的題目.Pptx
如何定義你的題目.Pptx
 
碩士論文寫作要點.Ppt
碩士論文寫作要點.Ppt碩士論文寫作要點.Ppt
碩士論文寫作要點.Ppt
 
如何定義你的題目.Pptx
如何定義你的題目.Pptx如何定義你的題目.Pptx
如何定義你的題目.Pptx
 
碩士論文寫作要點.Ppt
碩士論文寫作要點.Ppt碩士論文寫作要點.Ppt
碩士論文寫作要點.Ppt
 
Reviewing abstract
Reviewing abstractReviewing abstract
Reviewing abstract
 
Scott 校外口試
Scott 校外口試Scott 校外口試
Scott 校外口試
 
Scott Complex Networks
Scott Complex NetworksScott Complex Networks
Scott Complex Networks
 
Game Researches
Game ResearchesGame Researches
Game Researches
 

Último

Spring24-Release Overview - Wellingtion User Group-1.pdf
Spring24-Release Overview - Wellingtion User Group-1.pdfSpring24-Release Overview - Wellingtion User Group-1.pdf
Spring24-Release Overview - Wellingtion User Group-1.pdfAnna Loughnan Colquhoun
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6DianaGray10
 
Comparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and IstioComparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and IstioChristian Posta
 
Designing A Time bound resource download URL
Designing A Time bound resource download URLDesigning A Time bound resource download URL
Designing A Time bound resource download URLRuncy Oommen
 
PicPay - GenAI Finance Assistant - ChatGPT for Customer Service
PicPay - GenAI Finance Assistant - ChatGPT for Customer ServicePicPay - GenAI Finance Assistant - ChatGPT for Customer Service
PicPay - GenAI Finance Assistant - ChatGPT for Customer ServiceRenan Moreira de Oliveira
 
Introduction to Quantum Computing
Introduction to Quantum ComputingIntroduction to Quantum Computing
Introduction to Quantum ComputingGDSC PJATK
 
Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)Commit University
 
9 Steps For Building Winning Founding Team
9 Steps For Building Winning Founding Team9 Steps For Building Winning Founding Team
9 Steps For Building Winning Founding TeamAdam Moalla
 
COMPUTER 10: Lesson 7 - File Storage and Online Collaboration
COMPUTER 10: Lesson 7 - File Storage and Online CollaborationCOMPUTER 10: Lesson 7 - File Storage and Online Collaboration
COMPUTER 10: Lesson 7 - File Storage and Online Collaborationbruanjhuli
 
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...DianaGray10
 
Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemAsko Soukka
 
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UbiTrack UK
 
Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.YounusS2
 
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdfIaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdfDaniel Santiago Silva Capera
 
Things you didn't know you can use in your Salesforce
Things you didn't know you can use in your SalesforceThings you didn't know you can use in your Salesforce
Things you didn't know you can use in your SalesforceMartin Humpolec
 
Nanopower In Semiconductor Industry.pdf
Nanopower  In Semiconductor Industry.pdfNanopower  In Semiconductor Industry.pdf
Nanopower In Semiconductor Industry.pdfPedro Manuel
 
Cybersecurity Workshop #1.pptx
Cybersecurity Workshop #1.pptxCybersecurity Workshop #1.pptx
Cybersecurity Workshop #1.pptxGDSC PJATK
 
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IES VE
 
UiPath Community: AI for UiPath Automation Developers
UiPath Community: AI for UiPath Automation DevelopersUiPath Community: AI for UiPath Automation Developers
UiPath Community: AI for UiPath Automation DevelopersUiPathCommunity
 
Linked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesLinked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesDavid Newbury
 

Último (20)

Spring24-Release Overview - Wellingtion User Group-1.pdf
Spring24-Release Overview - Wellingtion User Group-1.pdfSpring24-Release Overview - Wellingtion User Group-1.pdf
Spring24-Release Overview - Wellingtion User Group-1.pdf
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6
 
Comparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and IstioComparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and Istio
 
Designing A Time bound resource download URL
Designing A Time bound resource download URLDesigning A Time bound resource download URL
Designing A Time bound resource download URL
 
PicPay - GenAI Finance Assistant - ChatGPT for Customer Service
PicPay - GenAI Finance Assistant - ChatGPT for Customer ServicePicPay - GenAI Finance Assistant - ChatGPT for Customer Service
PicPay - GenAI Finance Assistant - ChatGPT for Customer Service
 
Introduction to Quantum Computing
Introduction to Quantum ComputingIntroduction to Quantum Computing
Introduction to Quantum Computing
 
Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)
 
9 Steps For Building Winning Founding Team
9 Steps For Building Winning Founding Team9 Steps For Building Winning Founding Team
9 Steps For Building Winning Founding Team
 
COMPUTER 10: Lesson 7 - File Storage and Online Collaboration
COMPUTER 10: Lesson 7 - File Storage and Online CollaborationCOMPUTER 10: Lesson 7 - File Storage and Online Collaboration
COMPUTER 10: Lesson 7 - File Storage and Online Collaboration
 
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
 
Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystem
 
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
 
Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.
 
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdfIaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
 
Things you didn't know you can use in your Salesforce
Things you didn't know you can use in your SalesforceThings you didn't know you can use in your Salesforce
Things you didn't know you can use in your Salesforce
 
Nanopower In Semiconductor Industry.pdf
Nanopower  In Semiconductor Industry.pdfNanopower  In Semiconductor Industry.pdf
Nanopower In Semiconductor Industry.pdf
 
Cybersecurity Workshop #1.pptx
Cybersecurity Workshop #1.pptxCybersecurity Workshop #1.pptx
Cybersecurity Workshop #1.pptx
 
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
 
UiPath Community: AI for UiPath Automation Developers
UiPath Community: AI for UiPath Automation DevelopersUiPath Community: AI for UiPath Automation Developers
UiPath Community: AI for UiPath Automation Developers
 
Linked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesLinked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond Ontologies
 

4.1 network analysis basic

  • 2. • in-degree: how many directed edges (arcs) are incident on a node • out-degree: how may directed edges originate at a node • Degree sequence: [4, 4, 3, 7, …] Node properties
  • 3. Generated properties of node • Clustering coefficient: how your neighbors connected together • ego-density: density of the surrounding net UCINET: Network>Ego-networks>egonet basic measures
  • 4. • Directed or undirected • Weight, ranking, ... • Type, negative or positive, ... • Assigned-properties depending on calculating network itself, e.g., betweenness Edge properties
  • 5. Network Properties • Degree distribution: Frequency of degree sequences • Size: number of nodes (n) • Density: real relations divided by the maximum possible relations • Diameter: the length of the longest path • Average degree of separation:Average length of all possible shorted path UCINET: Network>Cohesion>Density
  • 6. Mode of network • One-mode network ‣ Friendship ‣ Collaboration e.g., User-paper represented by 2-mode network • Two-mode network—bipartite network ‣ User-borrowed book, co-bought ‣ Affiliation network— e.g., Member-Guild, Employee-Company
  • 9. • Degree How many resource do you have? • Closeness How far apart are you from others? • Betweenness How important are you for bridging sub-communities? • Centralization How balanced are actors’ centrality? Centrality Individual level Global level
  • 10. • Density How does the network tied together? • Separation, Diameter How far apart are you and your friends? • Cluster Coefficient How do your neighbors be connected? Individual level Global level
  • 11. Visualization through analysis process 1. Take a look 2. Analyze and find significant features such as sub- components or special positions 3. Draw the network according to the result of analysis 4. Color by the node features (e.g., sex, position, ...) and create hypothesis 5. Verify the hypothesis
  • 12. • Ego-network Analysis - - • Partial Network Analysis - One, two or three steps network two steps network - Boundary or sub-cluster of network • Whole Network Analysis - / Motif - Levels of network analysis
  • 13. • Data is recorded with a clear natural-occurring boundary and nodes in a boundary form a finite set. • What should be a possible boundary? ‣ A fixed location or room, specified time or day, a finite contact tracing, a formal group in an organization, a family. ‣ The boundary is known or decided firstly, a priori, to be a network. Policy of recording data
  • 14. • No sampling and tend to include all of the actors in some population(s). • Because network methods focus on relations among actors, actors cannot be sampled independently to be included as observations. Policy of recording data (2)
  • 15. • positivity A Priori metaphysics - • - • - - Butts, Carter T. "Revisiting the foundations of network analysis." Science325.5939 (2009): 414-416.
  • 16. • Closed Complete - finite set - • Singularity - • Consistency - Butts, Carter T. "Revisiting the foundations of network analysis." Science325.5939 (2009): 414-416.
  • 17. • Different relation sampling policy will cause different results—Threshold effects on network properties • Threshold Threshold • Threshold 0 Connectedness Betweenness • Threshold Betweenness Degree Betweenness • facebook 10 Application
  • 18. • Full network data is necessary to properly define and measure many of the structural concepts of network analysis (e.g. between-ness), however, very expensive. • Snowball methods begin with a focal actor or set of actors until no new actors are identified, or until we decide to stop. - Useful to track down “special” population such as business contact networks, community elites, deviant sub-cultures, avid stamp collectors, and kinship networks. - The snowball method may tend to overstate the "connectedness" and "solidarity" of populations of actors. - There is no guaranteed way of finding all of the connected individuals in the population. - How to select the first node (initial problem of sampling)? - Incomplete problem of the snowball methods can be solved by use of multiple initial nodes. Methods of sampling ties
  • 20. X Crossed-edges X Uninformed-edge length X Overlapped nodes and edges
  • 24. A B C D E F G H I J A 0 1 1 1 0 1 0 0 0 0 B 1 0 0 1 1 0 1 0 0 0 C 1 0 0 1 0 1 0 0 0 0 D 1 1 1 0 1 1 1 0 0 0 E 0 1 0 1 0 0 1 0 0 0 F 1 0 1 1 0 0 1 1 0 0 G 0 1 0 1 1 1 0 1 0 0 H 0 0 0 0 0 1 1 0 1 0 I 0 0 0 0 0 0 0 1 0 1 J 0 0 0 0 0 0 0 0 1 0 Adjacent matrix degree of B Symmetric M(1,4)=1, M(1,5)=0
  • 25. Homans(1951) Metrics representation and manipulation A B C D E F G H A 1 1 1 1 1 B 1 1 1 C 1 1 1 1 D 1 1 1 E 1 1 1 F 1 1 1 G 1 1 1 H 1 1 1 1 D E C H A B F G D 1 1 1 E 1 1 1 C 1 1 1 1 H 1 1 1 1 A 1 1 1 1 1 B 1 1 1 F 1 1 1 G 1 1 1