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Activity Number: 184 - SPEED: Variable Selection and Networks
Type: Contributed
Date/Time: Monday, July 31, 2017 : 11:35 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #325350
Title: Collaborative Spectral Clustering in Attributed Networks
Author(s): Xiaodong Jiang* and Pengsheng Ji
Companies: and University of Georgia
Keywords: Network Data Analysis ; Spectral Clustering ; Community Detection ; Attributed Networks ; Stochastic Block Model
Abstract:

We proposed a novel collaborative spectral clustering algorithm for attributed networks, where assuming n nodes splitting into R non-overlapping communities and each node has a p-dimensional meta covariate. The connectivity matrix W is constructed with the structural adjacent matrix A and the covariate matrix X, where W = (1 ? ?)A + ?K(X, X?), ? ? [0, 1] is a tuning parameter to balance between structural and covariate information, while K is a Kernel function, i.e., a similarity function over pairs of nodes in the covariate matrix. We then perform the classical k?means clustering algorithm on the element-wise ratio matrix of the first R leading eigenvectors of W. Both theoretical and simulation studies showed the consistent performance under Stochastic Block Model (SBM), especially in imbalanced networks where most community detection algorithms fail.


Authors who are presenting talks have a * after their name.

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