Tensor Spectral Clustering for Partitioning Higher-order Network Structures.
Spectral graph theory-based methods represent an important class of tools for studying the structure of networks. Spectral methods are based on a first-order Markov chain derived from a random walk on the graph and thus they cannot take advantage of important higher-order network substructures such as triangles, cycles, and feed-forward loops. Here we propose aTensor Spectral Clustering(TSC) algorithm that allows for modeling higher-order network structures in a graph partitioning framework. Our TSC algorithm allows the user to specify which higher-order network structures (cycles, feed-forward loops, etc.) should be preserved by the network clustering. Higher-order network structures of interest are represented using a tensor, which we then partition by developing a multilinear spectral method. Our framework can be applied to discovering layered flows in networks as well as graph anomaly detection, which we illustrate on synthetic networks. In directed networks, a higher-order structure of particular interest is the directed 3-cycle, which captures feedback loops in networks. We demonstrate that our TSC algorithm produces large partitions that cut fewer directed 3-cycles than standard spectral clustering algorithms.
Institute for Computational and Mathematical Engineering, Stanford University. Supported by Stanford Graduate Fellowship.
Austin R
AR
Benson
Department of Computer Science, Purdue University. Supported by NSF CAREER CCF-1149756 and IIS-1422918.
David F
DF
Gleich
Department of Computer Science, Stanford University. Supported by NSF IIS-1016909, CNS-1010921, IIS-1149837, ARO MURI, DARPA GRAPHS, PayPal, Docomo, Volkswagen, and Yahoo.
Jure
J
Leskovec