Link Prediction in Time Varying Social Networks

Vincenza Carchiolo,Marco Grassia,Michele Malgeri,Giuseppe Mangioni,Christian Cavallo

doi:10.3390/info13030123

Abstract

Predicting new links in complex networks can have a large societal impact. In fact, many complex systems can be modeled through networks, and the meaning of the links depend on the system itself. For instance, in social networks, where the nodes are users, links represent relationships (such as acquaintance, friendship, etc.), whereas in information spreading networks, nodes are users and content and links represent interactions, diffusion, etc. However, while many approaches involve machine learning-based algorithms, just the most recent ones account for the topology of the network, e.g., geometric deep learning techniques to learn on graphs, and most of them do not account for the temporal dynamics in the network but train on snapshots of the system at a given time. In this paper, we aim to explore Temporal Graph Networks (TGN), a Graph Representation Learning-based approach that natively supports dynamic graphs and assigns to each event (link) a timestamp. In particular, we investigate how the TGN behaves when trained under different temporal granularity or with various event aggregation techniques when learning the inductive and transductive link prediction problem on real social networks such as Twitter, Wikipedia, Yelp, and Reddit. We find that initial setup affects the temporal granularity of the data, but the impact depends on the specific social network. For instance, we note that the train batch size has a strong impact on Twitter, Wikipedia, and Yelp, while it does not matter on Reddit.

Highlights

In the last decades, the quantity of information about the environment that surrounds us is growing exponentially and has assumed more and more importance in our life, mainly due to the social networks that are the main actors in their production and dissemination
The paper discusses the application of a recent deep learning approach to the problem of link prediction in complex networks and the role of some of its hyperparameters
We performed several experiments aiming at predicting the evolving topology, i.e., creating and/or deleting links, based on previous events, on some dataset related to very popular social networks, such as Reddit, Wikipedia, Yelp, and Twitter

Summary

Introduction

The quantity of information about the environment that surrounds us is growing exponentially and has assumed more and more importance in our life, mainly due to the social networks that are the main actors in their production and dissemination. Since in many cases the existing topological correlation plays an important role in defining the social relations, Bronstein et al, in [8], report that, while deep learning has been successfully applied on a wide variety of tasks [9], from speech recognition [10,11] and machine translation [12] to image analysis and computer vision [13–15], there is a growing interest in trying to learn on non-Euclidean data. They list several open issues, such as generalization, support of directed graph, data synthesis, computation, and time-varying domains, the latter being the focus of this paper. Two of the most promising approaches are Temporal Graph Networks (TGN) [23] by Rossi et al and the Temporal Graph Attention (TGAT) [22] layer by Xu et al

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Conclusion