Abstract:

Network representation learning has played a critical role in studying networks. One way to study a graph is to focus on its spectrum, i.e., the eigenvalue distribution of its associated matrices. Recent advancements in spectral graph theory show that spectral moments of a network can be used to capture the network structure and various graph properties. However, sometimes networks with different structures or sizes can have the same or similar spectral moments, not to mention the existence of the cospectral graphs. To address such problems, we propose a 3D network representation that relies on the spectral information of subgraphs: the Spectral Path, a path connecting the spectral moments of the network and those of its subgraphs of different sizes. We show that the spectral path is interpretable and can capture relationship between a network and its subgraphs, for which we present a theoretical foundation. We demonstrate the effectiveness of the spectral path in applications such as network visualization and network identification.

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Recommended citation: Shengmin Jin, Hao Tian, Jiayu Li, and Reza Zafarani. “A Spectral Representation of Networks: The Path of Subgraphs.” In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 698-708. 2022.