Temporal Graph Kernels for Classifying Dissemination Processes

TL;DR

This paper introduces a framework for extending graph kernels to the temporal domain, enabling more accurate classification of dissemination processes in dynamic networks by capturing temporal information.

Contribution

It proposes three approaches for temporal graph kernels, analyzes their trade-offs, and introduces scalable stochastic variants with approximation guarantees.

Findings

Temporal kernels outperform static kernels in accuracy.

Proposed methods are scalable to large graphs.

Temporal information is crucial for classifying dissemination processes.

Abstract

Many real-world graphs or networks are temporal, e.g., in a social network persons only interact at specific points in time. This information directs dissemination processes on the network, such as the spread of rumors, fake news, or diseases. However, the current state-of-the-art methods for supervised graph classification are designed mainly for static graphs and may not be able to capture temporal information. Hence, they are not powerful enough to distinguish between graphs modeling different dissemination processes. To address this, we introduce a framework to lift standard graph kernels to the temporal domain. Specifically, we explore three different approaches and investigate the trade-offs between loss of temporal information and efficiency. Moreover, to handle large-scale graphs, we propose stochastic variants of our kernels with provable approximation guarantees. We evaluate our methods on a wide range of real-world social networks. Our methods beat static kernels by a large margin in terms of accuracy while still being scalable to large graphs and data sets. Hence, we confirm that taking temporal information into account is crucial for the successful classification of dissemination processes.