Title
New Algorithms for Counting Temporal Graph Pattern
Abstract
Temporal networks can describe multiple types of complex systems with temporal information in the real world. As an effective method for analyzing such network, temporal graph pattern (TGP) counting has received extensive attention and has been applied in diverse domains. In this paper, we study the problem of counting the TGP in the temporal network. Then, an exact algorithm is proposed based on the time first search (TFS) algorithm. This algorithm can reduce the intermediate results generated in the graph isomorphism and has high computational efficiency. To further improve the algorithm performance, we design an estimation algorithm by applying the edge sampling strategy to the exact algorithm. Finally, we evaluate the performances of the two algorithms by counting both the symmetric and asymmetric TGP. Extensive experiments on real datasets demonstrated that the exact algorithm is faster than the existing algorithm and the estimation algorithm can greatly reduce the running time while guaranteeing the accuracy.
Year
DOI
Venue
2019
10.3390/sym11101188
SYMMETRY-BASEL
Keywords
Field
DocType
temporal network,temporal graph pattern (TGP),TGP counting,edge sampling,TFS
Complex system,Graph,Graph isomorphism,Exact algorithm,Effective method,Algorithm,Sampling (statistics),Mathematics
Journal
Volume
Issue
Citations 
11
10
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Xiaoli Sun110.69
Yu-Song Tan23813.98
Qingbo Wu39211.73
Jing Wang401.35
Changxiang Shen512714.57