Network Classification in Temporal Networks Using Motifs

Understand

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.