Proceedings ArticleDOI
Influence and correlation in social networks
Aris Anagnostopoulos,Ravi Kumar,Mohammad Mahdian +2 more
- pp 7-15
TLDR
Two simple tests are proposed that can identify influence as a source of social correlation when the time series of user actions is available and are applied to real tagging data on Flickr, exhibiting that while there is significant social correlation in tagging behavior on this system, this correlation cannot be attributed to social influence.Abstract:
In many online social systems, social ties between users play an important role in dictating their behavior. One of the ways this can happen is through social influence, the phenomenon that the actions of a user can induce his/her friends to behave in a similar way. In systems where social influence exists, ideas, modes of behavior, or new technologies can diffuse through the network like an epidemic. Therefore, identifying and understanding social influence is of tremendous interest from both analysis and design points of view.This is a difficult task in general, since there are factors such as homophily or unobserved confounding variables that can induce statistical correlation between the actions of friends in a social network. Distinguishing influence from these is essentially the problem of distinguishing correlation from causality, a notoriously hard statistical problem.In this paper we study this problem systematically. We define fairly general models that replicate the aforementioned sources of social correlation. We then propose two simple tests that can identify influence as a source of social correlation when the time series of user actions is available.We give a theoretical justification of one of the tests by proving that with high probability it succeeds in ruling out influence in a rather general model of social correlation. We also simulate our tests on a number of examples designed by randomly generating actions of nodes on a real social network (from Flickr) according to one of several models. Simulation results confirm that our test performs well on these data. Finally, we apply them to real tagging data on Flickr, exhibiting that while there is significant social correlation in tagging behavior on this system, this correlation cannot be attributed to social influence.read more
Citations
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Proceedings ArticleDOI
The role of social networks in information diffusion
TL;DR: The authors examine the role of social networks in online information diffusion with a large-scale field experiment that randomizes exposure to signals about friends' information sharing among 253 million subjects in situ.
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De-anonymizing Social Networks
TL;DR: A framework for analyzing privacy and anonymity in social networks is presented and a new re-identification algorithm targeting anonymized social-network graphs is developed, showing that a third of the users who can be verified to have accounts on both Twitter and Flickr can be re-identified in the anonymous Twitter graph.
Journal ArticleDOI
Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks
TL;DR: A dynamic matched sample estimation framework is developed to distinguish influence and homophily effects in dynamic networks, and this framework is applied to a global instant messaging network of 27.4 million users, finding that previous methods overestimate peer influence in product adoption decisions in this network by 300–700%, and thathomophily explains >50% of the perceived behavioral contagion.
Proceedings ArticleDOI
Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter
TL;DR: The first large-scale validation of the "complex contagion" principle from sociology, which posits that repeated exposures to an idea are particularly crucial when the idea is in some way controversial or contentious, is provided.
Proceedings ArticleDOI
Learning influence probabilities in social networks
TL;DR: This paper proposes models and algorithms for learning the model parameters and for testing the learned models to make predictions, and develops techniques for predicting the time by which a user may be expected to perform an action.
References
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Proceedings ArticleDOI
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