study

1. Social Network Analysis with
Content and Graphs
徐凡耘

2. Resource
• Unit: LINCOLN LABORATORY JOURNAL
• Date: VOLUME 20, NUMBER 1, 2013
• Download:
https://www.ll.mit.edu/publications/journal/p
df/vol20_no1/20_1_5_Campbell.pdf

3. Authors
• William M. Campbell
– Match learning, mathematics in CU
• Charlie K. Dagli
– Computer engineering in UIUC
• Clifford J. Weinstein
– Electrical engineering in MIT

4. Agenda
• Introduction
– A consequence of changing economic
– Graph Construction
• Community Detection
– Modularity optimization
– Infomap
– Spectral Clustering
• Summary

5. We don’t cover
• Community Dynamics
– Latent semantic indexing(LSI)
– SVD
– Tensor
• Time-Profile-specific sub-network
– Classification(C4.5, PETS)
– Relational probability tree(RPT)

6. A consequence of
changing economic
• Big Data from variety and bunch of data
source of large-scale, real-world
sociographic data.
• Constructing social network, analyzing
the structure and dynamics of a
community and developing inferences
from social network

7. Graph Construction
• Challenge: too many factors
– Ambiguity of human language
– Multiple aliases for the same user
– Incompatible representations of information
– Ambiguity of relationship between
individuals

8. Data Source and
Information Extraction
• Newswire and sensor
– Smartphone and proximity devices provide
dynamic interactions
• Communications and social media
– Followers in Twitter, people who are
related by current news topics. Even FB…
– Email related to Enron’s bankruptcy

9. Introduction -
Information Extraction from Text
• Named-entity
recognition (NER)
extracts people, places
and orgs.
• Use links based upon
the co-occurrence of
entities in a documents
https://web.cs.umass.edu/publication/docs/2012/UM-CS-2012-015.pdf

10. Representation
• Knowledge Representation
– Participates(Bob, M)
Member(Bob, KarateClub)
– Ontology based on Automated Content
Extraction(ACE)

11. Community Detection
• High connectivity within a group and low
connectivity across groups
• Modularity
optimization(Clauset/Newman/Moore)
• Infomap
• Spectral clustering

12. DataSet
• Name: ISVG (Institute for the Study of
Violent Groups)
• Cover: terrorist and criminal activity
from open-source docs, including news
articles, court doc, police rpt.
• More than 100,000 incidents
• More than 1,500 hand-annotated types
• Nearly 30,000 individuals and 3,000
groups

13. Modularity optimization
(Clauset/Newman/Moore)
• Missing link prediction (e.g.
recommend friendship,
Folding@home)
• Using similarity probability
to associate nodes
• Strong similarity has
tendency to be linked?
(Men vs Men in sex-
network)

14. Modularity optimization
(Clauset/Newman/Moore)
• Even better performance in Terrorist
association and Grassland species network
• Both dataset has explicit level orgs.
• It is considered in large-scale network for big
calculating

15. • Convert a graph to Markov model by
random walk
• Using entropy to determine clusters
Infomap

16. Spectral Clustering (SC)
• Convergence in global optimal value with
arbitrary shape
• Few clusters, non-flat geometry
• Laplacian matrix for engenvalue/vector

17. Eigenvalue & Eigenvectors
• After image transformation, red line
keeps the same direction but yellow line
change to opposite direction
• Red line (eigenvalue = 1)
• Yellow line (eigenvalue = -1)
• They are orthogonal
• Av = λv (λ=Eigenvalue, v=Eigenvector)

18. Spectral Clustering (SC)
• Normalized cuts
– Divide and conquer
– NP-Complete

19. Precision and Recall
• Trade-off to fit the application

20. • Average color
• Segmentation
• Normalized Cut in Region Adjacency Graphs(RAG)
by removing edge
Normalized cuts

21. • L = D – A
Laplacian Matrix
A =
L =

22. Reference
• http://www.coolaler.com/showthread.php/184742-
%E4%BD%95%E8%AC%82-Folding-home-
%E8%9B%8B%E7%99%BD%E8%B3%AA%E6%91%BA%E7%
96%8A
• http://blog.sciencenet.cn/blog-3075-275710.html
• http://arxiv.org/pdf/0811.0484.pdf
• http://blog.sciencenet.cn/blog-329471-318268.html
• http://learnmath.pixnet.net/blog/post/44430775-
%E7%89%B9%E5%BE%B5%E5%80%BC%EF%BC%8C%E7
%89%B9%E5%BE%B5%E5%90%91%E9%87%8F
• http://www.fnlp.org/archives/4053
• http://dufu.math.ncu.edu.tw/calculus/calculus_eng/node162.html