Network Classification in Temporal Networks Using Motifs
It addresses the problem of classifying temporal networks for applications like community detection, but it is incremental as it builds on existing motif-based approaches.
The paper tackles network classification by proposing a method based on temporal motif distributions and null models, improving accuracy by up to 10% compared to state-of-the-art embedding methods in tasks like classifying network types and identifying communities.
Network classification has a variety of applications, such as detecting communities within networks and finding similarities between those representing different aspects of the real world. However, most existing work in this area focus on examining static undirected networks without considering directed edges or temporality. In this paper, we propose a new methodology that utilizes feature representation for network classification based on the temporal motif distribution of the network and a null model for comparing against random graphs. Experimental results show that our method improves accuracy by up $10\%$ compared to the state-of-the-art embedding method in network classification, for tasks such as classifying network type, identifying communities in email exchange network, and identifying users given their app-switching behaviors.