1. Discovering Important Nodes through Graph Entropy
Jitesh Shetty, Jafar Adibi
[KDD’ 05]
Advisor: Dr. Koh Jia-Ling
Reporter: Che-Wei, Liang
Date: 2008/09/18

2. Outline Introduction Order In Networks Graph Entropy
Experimental Result
Conclusions

3. Introduction
A new challenge in the area of Link Discovery and Social Network Analysis
To exploit communication pattern information and text information within knowledge discovery processes
such as discovery of hidden organizational structure and selection of interesting prominent members

4. Introduction Email logs Graph entropy
Prime importance and relevance in the study of information flow in an organization
Evidence database for law enforcement and intelligence organizations to detect hidden groups in an organization which are engaged in illegal activities
Graph entropy
To determine the most prominent interesting people

5. Order In Networks
A graph model might not be the best representation of organizations
Such as drug dealers, terrorist organization, threat groups
Usually ignore their hierarchy
They are composed of leaders and followers

6. Order In Networks
Example

7. Graph Entropy (1/6) To find prominent people in a network
Need to aggregate links between them and discover which node has the most effect on network
Entropy model can identify an entity that most effect on the graph entropy
Transform the problem space into a multigraph
Each node represents an entity, each link represents action between entities

8. Graph Entropy (2/6)

9. Graph Entropy (3/6)
Let G = (V, E) be a graph. P is the probability distribution on the vertex set V(G)
P(A B) =

10. Graph Entropy (4/6)
A great concern in LD domain is that elements of data are not independent
Ex: link Asend toB and link Bsend toC are dependent to each other, means B may forward A’s to C
Three approach to discover dependency
Examine the similarity of s
check

11. Graph Entropy (5/6) 3. Exploitation of Markov Blanket type of model
Assume an event(link) between two nodes is only dependent to those node’s events

12. Graph Entropy (6/6)

13. Experiment Enron Email Dataset
151 users, mostly senior management of Enron
contains 252,759 messages
Almost all users use folders to organize their s

14. Experiment

15. Experiment Created an Enron dictionary
Normalized all s using porter stemming algorithm
Compare the vectors using Jaccards Algorithm
Ordered s based on the time stamp

16. Experiment

17. Conclusions
Defined and addressed the problem of important nodes and finding closed group around them
Using event based entropy to find influential nodes in a graph and exhibit entropy model can act as a good means for detecting influential nodes