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Title: Uncovering Specific-Shape Graph Anomalies in Attributed Graphs
As networks are ubiquitous in the modern era, point anomalies have been changed to graph anomalies in terms of anomaly shapes. However, the specific-shape priors about anomalous subgraphs of interest are seldom considered by the traditional approaches when detecting the subgraphs in attributed graphs (e.g., computer networks, Bitcoin networks, and etc.). This paper proposes a nonlinear approach to specific-shape graph anomaly detection. The nonlinear approach focuses on optimizing a broad class of nonlinear cost functions via specific-shape constraints in attributed graphs. Our approach can be used in many different graph anomaly settings. The traditional approaches can only support linear cost functions (e.g., an aggregation function for the summation of node weights). However, our approach can employ more powerful nonlinear cost functions and enjoys a rigorous theoretical guarantee on the near-optimal solution with the geometrical convergence rate.  more » « less
Award ID(s):
1815696 1954409
PAR ID:
10110879
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)
Volume:
33
Issue:
01
Page Range / eLocation ID:
5433-5440
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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