Presented by: Eric Gavaletz
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1 Finding Effec ctors in Social Netw works T. Lappas (UCR), E. Terzi (BU), D. Gunopou ulos (UoA), H. Mannilaa (Aalto U.) Presented by: Eric Gavaletz 04/26/ /2011
2 The Motivation If we have as input the resulting activation state of anetwork... Keep in mind dth that tthings have already happened...
3 The Motivation Can you identify the most likely effectors? Or, can you find the nodes that could have started t 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 maximizati ion -- nodes that will cause the greatest propagation e ffect ++ 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 So ources: Simply ask people how much they are influenced by their peers, and machine le earning algorithms...
6 Effectors vs. Influence Maximization... k-effectors(0) = Influ uence Maximization In many cases the ese 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 sen nsitive propaganda
9 How about an ideal activation state? Triggering a regime change You know the supporters and the authoritarians Can you target effect tors so that the maximum supporters s 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 s. If it fails it does not try again.
12 Cost Given a set of effectors, X, Cost(X) = input vector - expected activations
13
14 Results 5508 DBLP Authors in Databases, Data Mining, Artificial Intelligence, and Theory
15 Results
16 Results
17 Qualitative Results Sets of effect tors included "very prolif ic authors" Five of the authors had papers each.
18 Follow-up Influence Maximization i in Soci ial 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 struct ture 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?
19 Follow-up Finding Influential l 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.
20 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 , Microsoft Research, [2] B. Chor and T. Tuller. Finding a maximum likelihood tree is hard. Journal of the ACM (JACM), 53(5): , [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 ACM, [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 ACM, 2011.
21 Questions?
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