Stochastic Opinion Dynamics for Interest Prediction in Social Networks
Marios Papachristou, Dimitris Fotakis

TL;DR
This paper introduces a stochastic model and an efficient algorithm for interest prediction in social networks, leveraging core-periphery structures and homophily to improve speed and accuracy over existing methods.
Contribution
It proposes the Nearest Neighbor Influence Model (NNIM) for interest formation and develops a scalable Variational EM algorithm for peripheral user interest prediction.
Findings
Algorithm runs up to 100 times faster than existing node embedding methods.
Achieves similar accuracy to state-of-the-art methods on benchmark networks.
Scales efficiently to networks with millions of nodes.
Abstract
We exploit the core-periphery structure and the strong homophilic properties of online social networks to develop faster and more accurate algorithms for user interest prediction. The core of modern social networks consists of relatively few influential users, whose interest profiles are publicly available, while the majority of peripheral users follow enough of them based on common interests. Our approach is to predict the interests of the peripheral nodes starting from the interests of their influential connections. To this end, we need a formal model that explains how common interests lead to network connections. Thus, we propose a stochastic interest formation model, the Nearest Neighbor Influence Model (NNIM), which is inspired by the Hegselmann-Krause opinion formation model and aims to explain how homophily shapes the network. Based on NNIM, we develop an efficient approach for…
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Taxonomy
TopicsComplex Network Analysis Techniques · Opinion Dynamics and Social Influence · Human Mobility and Location-Based Analysis
