1 / 26

Jing (Selena) He and Hisham M. Haddad

IPCCC 2013. Minimum-sized Positive Influential Node Set Selection for Social Networks: Considering Both Positive and Negative Influences. Jing (Selena) He and Hisham M. Haddad Department of Computer Science, Kennesaw State University Shouling Ji , Xiaojing Liao, and Raheem Beyah

ciara
Download Presentation

Jing (Selena) He and Hisham M. Haddad

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. IPCCC 2013 Minimum-sized Positive Influential Node Set Selection for Social Networks: Considering Both Positive and Negative Influences Jing (Selena) He and HishamM. Haddad Department of Computer Science, Kennesaw State University ShoulingJi, XiaojingLiao, and Raheem Beyah School of Electrical and Computer Engineering, Georgia Institute of Technology

  2. Outline • Motivation • Problem Definition • Greedy Algorithm • Performance Evaluation • Conclusions

  3. Motivation • Example & Applications • Related Work • Contributions

  4. Motivation Introduction • What is a social network? • The graph of relationships and interactions within a group of individuals.

  5. Social network plays a fundamental role as a medium for the spread of INFLUENCE among its members Opinions, ideas, information, innovation… Direct Marketing takes the “word-of-mouth” effects to significantly increase profits (facebook, twitter, myspace, …) Motivation Social Network and Spread of Influence

  6. Motivation Motivation •900 millionusers, Apr. 2012 •the3rdlargest― “Country”intheworld •MorevisitorsthanGoogle •Action:Updatestatues,createevent •Morethan4 billionimages •Action:Addtags,Addfavorites Socialnetworksalreadybecomeabridgetoconnect ourreallydailylifeandthevirtualwebspace •2009,2 billiontweetsperquarter •2010,4 billiontweetsperquarter •Action:Posttweets,Retweet

  7. Motivation Example Who are the opinion leaders in a community? Marketer Alice Find minimum-sized node (user) set in a social network that could positively influence on every node in the network

  8. Motivation Applications • Smoking intervention program • Promote new products • Advertising • Social recommendation • Expert finding • …

  9. Motivation Related Work • Influence Maximization (IM) Problem [Kempe03] • Select k nodes, maximize the expected number of influenced individuals • Positive Influence Dominating Set (PIDS) [Wang11] • Minimum-sized nominating set D, every other node has at least half of its neighbors in D

  10. Motivation Our Contributions • Consider both positive and negative influences • New optimization problem - Minimum-sized Positive Influential Node Set (MPINS) • Minimum-sized node set that could positively influence every node in the network no less than a threshold θ • Propose a greedy algorithm to solve MPINS • Conduct simulations to validate the proposed algorithm

  11. Problem Definition • Network Model • Diffusion Model • Problem Definition

  12. Problem Definition Network Model • A social network is represented as an undirected graph • Social influence represented by weights on the edges • Positive influence • Negative influence • Nodes start either active or inactive • An active node may trigger activation of neighboring nodes based on a pre-defined threshold θ • Monotonicity assumption: active nodes never deactivate

  13. Problem Definition Diffusion Model • Positive influence • Negative influence • Ultimate influence

  14. Problem Definition Minimum-sized Positive Influence Node Set (MPINS) • Given • a social network • a threshold θ • Goal • The initially selected active node set denoted by I could positively influence every other node in the network • , • Objective • Minimize the size of I

  15. Greedy Algorithm • Contribution function • Example • Correctness proof

  16. Greedy algorithm Contribution function

  17. Greedy algorithm Two-phase algorithm • Maximal Independent Set (M) • Greedy algorithm

  18. Greedy algorithm Example 18

  19. Greedy algorithm Correctness Proof

  20. Performance Evaluation • Simulation settings • Simulation results

  21. Performance Evaluation Simulation Settings • Generate random graph • The weighs on edges are randomly generated • For each specific setting, 100 instances are generated. The results are the average values of these 100 instances

  22. Performance Evaluation Simulation Results – small scale

  23. Performance Evaluation Simulation Results – Large scale

  24. Performance Evaluation Simulation Results

  25. Conclusions Conclusions • We study MPINS selection problem which has useful commercial applications in social networks. • We propose a greedy algorithm to solve the problem. • We validate the proposed algorithm through simulation on random graphs representing small size and large size networks.

  26. Q & A

More Related