Self-organizing flows in social networks

Abstract

Social networks offer users new means of accessing information, essentially relying on "social filtering", i.e. propagation and filtering of information by social contacts. The sheer amount of data flowing in these networks, combined with the limited budget of attention of each user, makes it difficult to ensure that social filtering brings relevant content to interested users. Our motivation in this paper is to measure to what extent self-organization of a social network results in efficient social filtering.To this end we introduce flow games, a simple abstraction that models network formation under selfish dynamics, featuring user-specific * Part of this work was done while at Technicolor. † Supported by the Inria project-team "Gang" in the "LIAFA" laboratory and by the "LINCS" laboratory. 1interests and budget of attention. In the context of homogeneous user interests, we show that selfish dynamics converge to a stable network structure (namely a pure Nash equilibrium) with close-to-optimal information dissemination. We show that, in contrast, for the more realistic case of heterogeneous interests, selfish dynamics may lead to information dissemination that can be arbitrarily inefficient, as captured by an unbounded "price of anarchy".Nevertheless the situation differs when user interests exhibit a particular structure, captured by a metric space with low doubling dimension. In that case, natural autonomous dynamics converge to a stable configuration. Moreover, users obtain all the information of interest to them in the corresponding dissemination, provided their budget of attention is logarithmic in the size of their interest set.

Modeling and Analysis of Information Communities

“…Related to the analysis in this paper is the work on content forwarding and filtering in social networks [2,5,6]. In particular the work by Zadeh, Goel and Munagala [2], and the work by Hegde, Massoulie, and Viennot [6].…”

“…Related to the analysis in this paper is the work on content forwarding and filtering in social networks [2,5,6]. In particular the work by Zadeh, Goel and Munagala [2], and the work by Hegde, Massoulie, and Viennot [6]. In [2], Zadeh, Goel and Munagala consider the problem of information diffusion in social networks under a broadcast model where content forwarded (posted) by a user is seen by all its neighbors (followers, friends) in the social graph.…”

“…The main result in [2] shows that this is indeed the case under suitable graph models such as for example Kronecker graphs. In [6], Hegde, Massoulie, and Viennot study the problem where users are interested in obtaining content on specific topics, and study whether there exists a graph structure and filtering strategy that allows users to obtain all the content they are interested in. Using a game-theoretic framework (flow games), the analysis in [6] shows that under suitable assumptions there exists a Nash equilibrium, and selfish dynamics converge to a Nash equilibrium.…”

“…In [6], Hegde, Massoulie, and Viennot study the problem where users are interested in obtaining content on specific topics, and study whether there exists a graph structure and filtering strategy that allows users to obtain all the content they are interested in. Using a game-theoretic framework (flow games), the analysis in [6] shows that under suitable assumptions there exists a Nash equilibrium, and selfish dynamics converge to a Nash equilibrium. The main difference between the model and analysis in [2,6] and the approach in this paper is that model and analysis in [2,6] does not explicitly consider and model community structures, and the utility obtained by users under the models in [2,6] depends only on the content that agents receive, but not on the content agents produce.…”

Complexity of a Microblogging Social Network in the Framework of Modern Nonlinear Science

“…Despite having a variety of sociophysical models, the results and theories of nonlinear science, with some 2 Complexity exclusions (see, e.g., [20,21]), are not used to model the evolution of social networks. First of all, we are talking about the complexity and self-organized criticality theory describing the mechanism of complexity [22][23][24].…”