Stochastic Opinion Dynamics for User Interest Prediction in Online Social Networks

Presenter: Marios Papachristou
Date: 03 July 2020


In this seminar, we are going to talk about how one can infer the interests (e.g. hobbies) of users in online social networks using information from highly influential users of the network. More specifically, we experimentally observe that the majority of the network users (>70%) is dominated by a sublinear fraction of highly-influential nodes (core nodes). This structural property of networks is also known as the "core-periphery" structure, a phenomenon long-studied in economics and sociology.

Using the influencers' initial opinions as steady-state trend-setters, we develop a generative model through which we explain how the users' interests (opinions) evolve over time, where each peripheral user looks at her k-nearest neighbors. Our model has strong theoretical and experimental guarantees and is able to surpass node embedding methods and related opinion dynamics methods and is able to scale to networks with millions of nodes.

Duration: 30-40min.

Joint work with D. Fotakis (NTUA).