A Survey on Subgraph Counting: Concepts, Algorithms and Applications to Network Motifs and Graphlets
arXiv:1910.13011 · doi:10.1145/3433652
Abstract
Computing subgraph frequencies is a fundamental task that lies at the core of several network analysis methodologies, such as network motifs and graphlet-based metrics, which have been widely used to categorize and compare networks from multiple domains. Counting subgraphs is however computationally very expensive and there has been a large body of work on efficient algorithms and strategies to make subgraph counting feasible for larger subgraphs and networks. This survey aims precisely to provide a comprehensive overview of the existing methods for subgraph counting. Our main contribution is a general and structured review of existing algorithms, classifying them on a set of key characteristics, highlighting their main similarities and differences. We identify and describe the main conceptual approaches, giving insight on their advantages and limitations, and provide pointers to existing implementations. We initially focus on exact sequential algorithms, but we also do a thorough survey on approximate methodologies (with a trade-off between accuracy and execution time) and parallel strategies (that need to deal with an unbalanced search space).
35 pages
References in corpus (7)
- Biological network comparison using graphlet degree distribution
- ESCAPE: Efficiently Counting All 5-Vertex Subgraphs
- Path Sampling: A Fast and Provable Method for Estimating 4-Vertex Subgraph Counts
- Variational principle for scale-free network motifs
- Subgraph covers -- An information theoretic approach to motif analysis in networks
- Beyond Triangles: A Distributed Framework for Estimating 3-profiles of Large Graphs
- Higher-Order Ranking and Link Prediction: From Closing Triangles to Closing Higher-Order Motifs
Cited by in corpus (8)
- A Network Science perspective of Graph Convolutional Networks: A survey
- Human Mobility Networks Manifest Dissimilar Resilience Characteristics at Macroscopic, Substructure, and Microscopic Scales
- DeSCo: Towards Generalizable and Scalable Deep Subgraph Counting
- Comparing directed networks via denoising graphlet distributions
- Locating highly connected clusters in large networks with HyperLogLog counters
- Enumerating Graphlets with Amortized Time Complexity Independent of Graph Size
- BioGraphletQA: Knowledge-Anchored Generation of Complex QA Datasets
- A systematic association of subgraph counts over a network