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 have already happened...

The Motivation

Can you identifythe most likely effectors?

Or, can you find the nodes that could have started a propagation leading to the observed state?

:~$ 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

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...

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...

Targeting niche markets

Technologically savvy groups

Tightly knit subcultures

Unwanted propagation is damaging

Targeted social networking 

Hippster and Independent groups

Subversive and sensitive propaganda

How about an ideal activation state?

Triggering a regime change

You know the supportersand the authoritarians…

Can you target effectors so that the maximum supporters and minimum authoritarians get the message?

Formal Problem Specification

IndependentCascadePropagationModel

An active nodegets one shot to activate a neighbors.If it fails it does not try again.

Cost

Given a set of effectors, X,

Cost(X) = input vector - expected activations

Results

5508 DBLP Authors in 

Databases, Data Mining, Artificial Intelligence,

and Theory

Results

Results

Qualitative Results

Sets of effectors included "very prolific authors"

Five of the authors had 156 - 250 papers each.

Follow-up

Influence Maximization in Social Networks WhenNegative Opinions May Emerge and Propagate

Wei Chen et al.*

Lappas et al. [14] study k-effectors problem, which containsinfluence 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 witha tree structure. The difference, besides not considering thenegative opinion, is that they use one tree structure...

* 10 authors for a 10 page paper?

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.

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.

Questions?