1 / 21

Finding Effectors in Social Networks

Finding Effectors in Social Networks. T. Lappas (UCR), E. Terzi (BU),  D. Gunopoulos (UoA),  H. Mannila (Aalto U.) Presented by: Eric Gavaletz 04/26/2011. The Motivation. If we have as input  the resulting  activation state  of a network... Keep in mind that things 

maddox
Download Presentation

Finding Effectors in Social Networks

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. Finding Effectors in Social Networks T. Lappas (UCR), E. Terzi (BU),  D. Gunopoulos (UoA),  H. Mannila (Aalto U.) Presented by: Eric Gavaletz 04/26/2011

  2. The Motivation If we have as input  the resulting  activation state  of a network... Keep in mind that things  have already happened...

  3. The Motivation Can you identify the most likely  effectors? Or, can you find the nodes that could have started a propagation leading to the observed state?

  4. :~$ diff effectors sota top-k influential nodes    -- metric determining a node's       influence potential     ++ how groups of effectors       collectively affect the network influence maximization     -- nodes that will cause the       greatest propagation effect     ++ nodes that best explain an       observed pattern

  5. Effectors vs. top-k influential... -- no influential potential ++ info-propagation model ++ influence weights as input Suggested Input Sources: Simply ask people how much they are influenced by their peers, and machine learning algorithms...

  6. Effectors vs. Influence Maximization... k-effectors(0) = Influence Maximization In many cases these problems are similar...but they are different in some very interesting cases...

  7. Targeting niche markets Technologically savvy groups Tightly knit subcultures

  8. Unwanted propagation is damaging Targeted social networking  Hippster and Independent groups Subversive and sensitive propaganda

  9. How about an ideal activation state? Triggering a regime change You know the supporters and the authoritarians… Can you target effectors so that the maximum supporters and minimum authoritarians get the message?

  10. Formal Problem Specification

  11. Independent Cascade Propagation Model An active node gets one shot to  activate a neighbors. If it fails it does not try again.

  12. Cost Given a set of  effectors, X, Cost(X) = input vector -  expected activations

  13. Results 5508 DBLP Authors in  Databases, Data Mining,  Artificial Intelligence, and Theory

  14. Results

  15. Results

  16. Qualitative Results Sets of effectors included "very prolific authors" Five of the authors had 156 - 250 papers each.

  17. Follow-up Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate Wei Chen et al.* Lappas et al. [14] study k-effectors problem, which contains influence maximization (without negative opinions) as a special case. They also use a tree structure to make the computation tractable, and then approximate the original graph with a tree structure. The difference, besides not considering the negative opinion, is that they use one tree structure... * 10 authors for a 10 page paper?

  18. Follow-up Finding Influential Mediators in Social Networks Cheng-Te Li et al.* Lappas et al. [5] propose to find a set of  effectors who can cause an activation pattern as similar as possible to the given active nodes in a social network.

  19. REFERENCES [1] W. Chen, A. Collins, R. Cummings, T. Ke, Z. Liu, D. Rincon, X. Sun, Y. Wang, W. Wei, and Y. Yuan. Influence maximization in social networks when negative opinions may emerge and propagate. Technical report, Technical Report MSR-TR-2010-137, Microsoft Research, 2010. [2] B. Chor and T. Tuller. Finding a maximum likelihood tree is hard. Journal of the ACM (JACM), 53(5):722–744, 2006. [3] T. Lappas, E. Terzi, D. Gunopulos, and H. Mannila. Finding effectors in social networks. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1059–1068. ACM, 2010. [4] C.T. Li, S.D. Lin, and M.K. Shan. Finding influential mediators in social networks. In Proceedings of the 20th international conference companion on World wide web, pages 75–76. ACM, 2011.

  20. Questions?

More Related