1 / 17

Discovering Important Nodes through Graph Entropy

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

Download Presentation

Discovering Important Nodes through Graph Entropy

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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 • 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(AemailB) =

  10. Graph Entropy (4/6) • A great concern in LD domain is that elements of data are not independent • Ex: link AsendemailtoB and link BsendemailtoC are dependent to each other, means B may forward A’s email to C • Three approach to discover dependency • Examine the similarity of emails • 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 email messages • Almost all users use folders to organize their emails

  14. Experiment

  15. Experiment • Created an Enron dictionary • Normalized all emails using porter stemming algorithm • Compare the vectors using Jaccards Algorithm • Ordered emails 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

More Related