Learning User Representations in Online Social Networks using Temporal Dynamics of Information Diffusion

TL;DR

This paper introduces a method to learn user representations in social networks based on temporal topic adoption data, addressing limitations of network structure reliance and demonstrating effectiveness in prediction tasks.

Contribution

It proposes a novel approach leveraging temporal topic adoption to embed users, bypassing the need for complete or current network structure information.

Findings

Effective in preserving link structure information

Improves prediction of future topic adopters

Enhances geo-location prediction accuracy

Abstract

This article presents a novel approach for learning low-dimensional distributed representations of users in online social networks. Existing methods rely on the network structure formed by the social relationships among users to extract these representations. However, the network information can be obsolete, incomplete or dynamically changing. In addition, in some cases, it can be prohibitively expensive to get the network information. Therefore, we propose an alternative approach based on observations from topics being talked on in social networks. We utilise the time information of users adopting topics in order to embed them in a real-valued vector space. Through extensive experiments, we investigate the properties of the representations learned and their efficacy in preserving information about link structure among users. We also evaluate the representations in two different prediction tasks, namely, predicting most likely future adopters of a topic and predicting the geo-location of users. Experiments to validate the proposed methods are performed on a large-scale social network extracted from Twitter, consisting of about 7.7 million users and their activity on around 3.6 million topics over a month-long period.