Networks are known as perfect tools for modeling various types of systems. In the literature of network mining, frequent subgraph mining is considered as the essence of mining network data. In this problem, the dataset is composed of networks representing multiple independent systems or one system at multiple time stamps. The cores of mining frequent subgraphs are graph and subgraph isomorphism. Due to the complexities of these problems, the frequent subgraph mining algorithms proposed in the literature employ various heuristics for candidate generation, duplicate subgraphs pruning, and support computation. In this survey, we provide a classification of proposed algorithms in the literature. The algorithms for static networks have found numerous applications. Therefore, these algorithms will be reviewed in detail. Besides, it is discussed that consideration of temporality of data can impact the derived insight and attracted substantial attention in recent years. However, prior surveys have not comprehensively examined the algorithms of frequent subgraph mining in a database of temporal networks represented as network snapshots. Therefore, the algorithms proposed for mining frequent subgraphs in temporal networks are reviewed. Moreover, most of the surveys have focused on main-memory algorithms. Here, we review disk-based, parallel, and distributed algorithms proposed for mining frequent subgraphs.
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Motif discovery algorithms in static and temporal networks: A survey
Abstract Motifs are the fundamental components of complex systems. The topological structure of networks representing complex systems and the frequency and distribution of motifs in these networks are intertwined. The complexities associated with graph and subgraph isomorphism problems, as the core of frequent subgraph mining, directly impact the performance of motif discovery algorithms. Researchers have adopted different strategies for candidate generation and enumeration and frequency computation to cope with these complexities. Besides, in the past few years, there has been an increasing interest in the analysis and mining of temporal networks. In contrast to their static counterparts, these networks change over time in the form of insertion, deletion or substitution of edges or vertices or their attributes. In this article, we provide a survey of motif discovery algorithms proposed in the literature for mining static and temporal networks and review the corresponding algorithms based on their adopted strategies for candidate generation and frequency computation. As we witness the generation of a large amount of network data in social media platforms, bioinformatics applications and communication and transportation networks and the advance in distributed computing and big data technology, we also conduct a survey on the algorithms proposed to resolve the CPU-bound and I/O bound problems in mining static and temporal networks.
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- NSF-PAR ID:
- 10304039
- Editor(s):
- Holme, Peter
- Date Published:
- Journal Name:
- Journal of Complex Networks
- Volume:
- 8
- Issue:
- 4
- ISSN:
- 2051-1310
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/comnet/cnaa031
