1 / 22

Influence and Correlation in Social Networks

This presentation delves into the concepts of social influence and correlation within social networks, exploring their distinct yet interrelated nature. It examines how individuals interact, share advice, and the various driving factors behind social behavior. Through experimental evaluations, including influence and correlation models, we assess the dynamics of social connections and the effects of homophily and confounding factors. The future direction includes investigating stable relationships in online communities and their evolution over time, revealing deeper insights into social network interactions.

shaun
Télécharger la présentation

Influence and Correlation 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. Influence and Correlation in Social Networks Xufeiwang Nov-7-2008

  2. Outline • Background, Concepts • Problem statement • Basic idea • Experimental Evaluation • Future directions

  3. Proofs of social correlation • People interact with others • Advices, reading, commenting • Communicating with others • Non-causal correlation • Both the CO2 level and crime level have increased sharply • Both beer and diaper sales well in a super market • Causal correlation • I bought an IPhone after I’m recommended by my friend

  4. Social influence • A bought an IPhoneafter B told him it’s cool • Directed: B influences A, not A influences B • Chronological: A is influenced after B told him • Asymmetry: B has influence to A doesn’t imply A has the same influence to B

  5. Sources of correlation • Socialinfluence: One person performing an action can cause her contacts to do the same. • A bought an IPhoneafter B told him it’s cool • Homophily: Similar individuals are more likely to become friends. • Example: two mathematicians are more likely to become friends. • Confoundingfactors: External influence from elements in the environment. • Example: friends live in the same area, thus attend and take pictures of similar events, and tag them with similar tags.

  6. Outline • Background, Concepts • Problem statement • Basic idea • Experimental Evaluation • Future directions

  7. Problem statement • Social correlation and social influence are different concepts • Are they related? • Maybe yes and Maybe no

  8. Outline • Background, Concepts • Problem statement • Basic idea • Experimental Evaluation • Future directions

  9. Social correlation evaluation • Influence model: each agent becomes active in each time step independently with probability p(a), where a is the # of active friends. • Natural choice for p(a): logistic regression function: with ln(a+1) as the explanatory variable. I.e., • Coefficient α measures social correlation.

  10. Testing for influence • Shuffle Test: • Chronological property • Edge-Reversal Test: • Asymmetry property C C A A B B

  11. Outline • Background, Concepts • Problem statement • Basic idea • Experimental Evaluation • Future directions

  12. Experimental setup • Influence model • Only use the influence factor • Current node A has “a” active friends, its probability to be active is related with the # of active friends • Correlation model • Use the homophily and confounding factors • Init S nodes as centers randomly, add a ball of radius 2 to each node in S, according to the data on Flickr, randomly pick the same # of nodes to be active

  13. Simulation results Shuffle test, influence model

  14. Simulation results Edge-reversaltest, influence model

  15. Simulation results Shuffle test, correlation model

  16. Simulation results Edge-reversaltest, correlation model

  17. Shuffle test on Flickr data

  18. Edge-reversal test on Flickr data

  19. Explanations • The users’ tagging actions are independent • The users either seldom visit their friends’ pages • Or the users visit pages but only care about the content rather than the tags

  20. Outline • Background, Concepts • Problem statement • Basic idea • Experimental Evaluation • Future directions

  21. Future directions I • The relationship in the internet is weak! • How weak it is? • So I think it’s interesting to search close communities, based on strong correlation, in blogosphere • How to define the “strongness” • How the “strongness” among the users • Do we have reasonable datasets • “strongness” is related with time?

  22. Future Directions II • Most of the users don’t contact frequently • How about the contact distribution • Search for stable relationships is also interesting. Seeking stable communities • How to define stable? • Stable relationship can be strong or weak connection • Contact infrequently but regularly • The group can be small • Hold for a long time??

More Related