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Title: Block-Structured Optimization for Anomalous Pattern Detection in Interdependent Networks
We propose a generalized optimization framework for detecting anomalous patterns (subgraphs that are interesting or unexpected) in interdependent networks, such as multi-layer networks, temporal networks, networks of networks, and many others. We frame the problem as a non-convex optimization that has a general nonlinear score function and a set of block-structured and non-convex constraints. We develop an effective, efficient, and parallelizable projection-based algorithm, namely Graph Block-structured Gradient Projection (GBGP), to solve the problem. It is proved that our algorithm 1) runs in nearly-linear time on the network size, and 2) enjoys a theoretical approximation guarantee. Moreover, we demonstrate how our framework can be applied to two very practical applications, and we conduct comprehensive experiments to show the effectiveness and efficiency of our proposed algorithm.  more » « less
Award ID(s):
1954409
NSF-PAR ID:
10187144
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
2019 IEEE International Conference on Data Mining (ICDM)
Page Range / eLocation ID:
1138 to 1143
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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