Motif Discovery Algorithms in Static and Temporal Networks: A Survey

  title={Motif Discovery Algorithms in Static and Temporal Networks: A Survey},
  author={Ali Jazayeri and Christopher C. Yang},
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, have direct impacts on the performance of motif discovery algorithms. To cope with these complexities, researchers have adopted different strategies for candidate generation and… Expand
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odeN: Simultaneous Approximation of Multiple Motif Counts in Large Temporal Networks
OdeN, a sampling-based algorithm that provides an accurate approximation of all the counts of the motifs in temporal networks in a fraction of the time needed by state-of-the-art methods, and that it also reports more accurate approximations than such methods. Expand
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  • Ilie Sarpe, Fabio Vandin
  • Proceedings of the 30th ACM International Conference on Information & Knowledge Management
  • 2021


Trend Motif: A Graph Mining Approach for Analysis of Dynamic Complex Networks
This work has developed efficient mining algorithms to extract trend motifs, a trend motif describes a recurring subgraph where each of its vertices or edges displays similar dynamics over a user- defined period. Expand
MODA: an efficient algorithm for network motif discovery in biological networks.
This paper presents a new algorithm (MODA) that incorporates techniques such as a pattern growth approach for extracting larger motifs efficiently and is able to identifylarger motifs with more than 8 nodes more efficiently than most of the current state-of-the-art motif discovery algorithms. Expand
Kavosh: a new algorithm for finding network motifs
A new algorithm, Kavosh, for finding k-size network motifs with less memory and CPU time in comparison to other existing algorithms, based on counting all k- size sub-graphs of a given graph (directed or undirected). Expand
Performance Evaluation of Frequent Subgraph Discovery Techniques
The objective of this research work is to perform quantitative comparison of the above listed techniques based on three different state-of-the-art graph datasets to provide base for anyone who is working to design a new frequent subgraph discovery technique. Expand
Network Motif Discovery Using Subgraph Enumeration and Symmetry-Breaking
A novel algorithm for discovering large network motifs that achieves these goals, based on a novel symmetry-breaking technique, which eliminates repeated isomorphism testing, leading to an exponential speed-up over previous methods. Expand
Counting motifs in dynamic networks
A scalable method for counting the number of motifs in a dynamic biological network that incrementally updates the frequency of each motif as the underlying network’s topology evolves and can be scaled to large dense networks. Expand
Strategies for Network Motifs Discovery
A review and runtime comparison of current motif detection algorithms in the field and categorize the algorithms outlining the main differences and advantages of each strategy to allow a fair and objective efficiency comparison using a set of benchmark networks. Expand
Motifs in Temporal Networks
A notion of a temporal network motif as an elementary unit of temporal networks is developed and a general methodology for counting such motifs is provided and it is found that measuring motif counts at various time scales reveals different behavior. Expand
Building blocks of biological networks: a review on major network motif discovery algorithms.
The authors will give a review on computational aspects of major algorithms and enumerate their related benefits and drawbacks from an algorithmic perspective. Expand
Efficient mining of frequent subgraphs in the presence of isomorphism
This work proposes a novel frequent subgraph mining algorithm: FFSM, which employs a vertical search scheme within an algebraic graph framework it has developed to reduce the number of redundant candidates proposed. Expand