Concept Drift and Anomaly Detection in Graph Streams
Additional information
Type
Journal Article
Year
2018
Language
English
Abstract
Graph representations offer powerful and intuitive ways to describe data in a multitude of application domains. Here, we consider stochastic processes generating graphs and propose a methodology for detecting changes in stationarity of such processes. The methodology is general and considers a process generating attributed graphs with a variable number of vertices/edges, without the need to assume a one-to-one correspondence between vertices at different time steps. The methodology acts by embedding every graph of the stream into a vector domain, where a conventional multivariate change detection procedure can be easily applied. We ground the soundness of our proposal by proving several theoretical results. In addition, we provide a specific implementation of the methodology and evaluate its effectiveness on several detection problems involving attributed graphs representing biological molecules and drawings. Experimental results are contrasted with respect to suitable baseline methods, demonstrating the effectiveness of our approach.
Journal
IEEE Transactions on Neural Networks and Learning Systems
Volume
29
Number
11
Start page number
5592
End page number
5605
ISSN
2162-2388
Keywords
anomaly detection, attributed graph, change detection, concept drift, dynamic/evolving graph, embedding, graph matching, stationarity
