1. On analyzing graphs with motif-paths
- Author
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Reynold Cheng, Kevin Chen-Chuan Chang, Hongtai Cao, Chenhao Ma, Caihua Shan, and Xiaodong Li
- Subjects
0301 basic medicine ,Power graph analysis ,Theoretical computer science ,Computer science ,General Engineering ,02 engineering and technology ,Link (geometry) ,03 medical and health sciences ,ComputingMethodologies_PATTERNRECOGNITION ,030104 developmental biology ,020204 information systems ,Path (graph theory) ,0202 electrical engineering, electronic engineering, information engineering ,Motif (music) ,Clustering coefficient - Abstract
Path-based solutions have been shown to be useful for various graph analysis tasks, such as link prediction and graph clustering. However, they are no longer adequate for handling complex and gigantic graphs. Recently, motif-based analysis has attracted a lot of attention. A motif, or a small graph with a few nodes, is often considered as a fundamental unit of a graph. Motif-based analysis captures high-order structure between nodes, and performs better than traditional "edge-based" solutions. In this paper, we study motif-path , which is conceptually a concatenation of one or more motif instances. We examine how motif-paths can be used in three path-based mining tasks, namely link prediction, local graph clustering and node ranking. We further address the situation when two graph nodes are not connected through a motif-path, and develop a novel defragmentation method to enhance it. Experimental results on real graph datasets demonstrate the use of motif-paths and defragmentation techniques improves graph analysis effectiveness.
- Published
- 2021