TY - GEN

T1 - A Spectral Representation of Networks

T2 - 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022

AU - Jin, Shengmin

AU - Tian, Hao

AU - Li, Jiayu

AU - Zafarani, Reza

N1 - Publisher Copyright:
© 2022 ACM.

PY - 2022/8/14

Y1 - 2022/8/14

N2 - 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.

AB - 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.

KW - network representations

KW - spectral graphs

UR - http://www.scopus.com/inward/record.url?scp=85137148834&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85137148834&partnerID=8YFLogxK

U2 - 10.1145/3534678.3539433

DO - 10.1145/3534678.3539433

M3 - Conference contribution

AN - SCOPUS:85137148834

T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

SP - 698

EP - 708

BT - KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

PB - Association for Computing Machinery

Y2 - 14 August 2022 through 18 August 2022

ER -