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Time-Variant Graph Classification.

Authors :
Wang, Haishuai
Wu, Jia
Zhu, Xingquan
Chen, Yixin
Zhang, Chengqi
Source :
IEEE Transactions on Systems, Man & Cybernetics. Systems; Aug2020, Vol. 50 Issue 8, p2883-2896, 14p
Publication Year :
2020

Abstract

Graphs are commonly used to represent objects, such as images and text, for pattern classification. In a dynamic world, an object may continuously evolve over time, and so does the graph extracted from the underlying object. These changes in graph structure with respect to the temporal order present a new representation of the graph, in which an object corresponds to a set of time-variant graphs. In this paper, we formulate a novel time-variant graph classification task and propose a new graph feature, called a graph-shapelet pattern, for learning and classifying time-variant graphs. Graph-shapelet patterns are compact and discriminative graph transformation subsequences. A graph-shapelet pattern can be regarded as a graphical extension of a shapelet—a class of discriminative features designed for vector-based temporal data classification. To discover graph-shapelet patterns, we propose to convert a time-variant graph sequence into time-series data and use the discovered shapelets to find graph transformation subsequences as graph-shapelet patterns. By converting each graph-shapelet pattern into a unique tokenized graph transformation sequence, we can measure the similarity between two graph-shapelet patterns and therefore classify time-variant graphs. Experiments on both synthetic and real-world data demonstrate the superior performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
21682216
Volume :
50
Issue :
8
Database :
Complementary Index
Journal :
IEEE Transactions on Systems, Man & Cybernetics. Systems
Publication Type :
Academic Journal
Accession number :
144714764
Full Text :
https://doi.org/10.1109/TSMC.2018.2830792