Temporal Graph Kernels for Classifying Dissemination Processes

Abstract Many real-world graphs are temporal, e.g., in a social network persons only interact at specific points in time. This temporality directs possible dissemination processes on the graph, 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. 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 various 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. This confirms that taking temporal information into account is crucial for the successful classification of temporal graphs under consideration of dissemination processes.