Temporal Graph Kernels for Classifying Dissemination Processes
This work addresses the challenge of classifying dissemination processes like rumor or disease spread in temporal networks, which is incremental as it extends existing graph kernels to handle temporal data.
The authors tackled the problem of classifying dissemination processes on temporal graphs, where existing static graph kernels fail to capture temporal information, and introduced a framework to lift standard graph kernels to the temporal domain with stochastic variants for scalability. Their methods significantly outperformed static kernels in accuracy on real-world social networks, demonstrating the importance of temporal information for classification.
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.