FSM: Fast and scalable network motif discovery for exploring higher-order network organizations

Methods ◽  
2020 ◽  
Vol 173 ◽  
pp. 83-93 ◽  
Author(s):  
Tao Wang ◽  
Jiajie Peng ◽  
Qidi Peng ◽  
Yadong Wang ◽  
Jin Chen
Keyword(s):  
Motif Discovery ◽  
Network Motif ◽  
Higher Order ◽  
Network Organizations ◽  
Scalable Network

Motif discovery in biological network using expansion tree

2018 ◽  
Vol 16 (06) ◽  
pp. 1850024 ◽  
Author(s):  
Sabyasachi Patra ◽  
Anjali Mohapatra
Keyword(s):  
Complex Networks ◽  
Protein Interaction ◽  
Motif Discovery ◽  
Statistical Significance ◽  
Network Motif ◽  
Graph Isomorphism ◽  
Building Blocks ◽  
Network Motifs ◽  
Topological Features ◽  
Scalable Network

Networks are powerful representation of topological features in biological systems like protein interaction and gene regulation. In order to understand the design principles of such complex networks, the concept of network motifs emerged. Network motifs are recurrent patterns with statistical significance that can be seen as basic building blocks of complex networks. Identification of network motifs leads to many important applications, such as understanding the modularity and the large-scale structure of biological networks, classification of networks into super-families, protein function annotation, etc. However, identification of network motifs is challenging as it involves graph isomorphism which is computationally hard. Though this problem has been studied extensively in the literature using different computational approaches, we are far from satisfactory results. Motivated by the challenges involved in this field, an efficient and scalable network Motif Discovery algorithm based on Expansion Tree (MODET) is proposed. Pattern growth approach is used in this proposed motif-centric algorithm. Each node of the expansion tree represents a non-isomorphic pattern. The embeddings corresponding to a child node of the expansion tree are obtained from the embeddings of the parent node through vertex addition and edge addition. Further, the proposed algorithm does not involve any graph isomorphism check and the time complexities of these processes are [Formula: see text] and [Formula: see text], respectively. The proposed algorithm has been tested on Protein–Protein Interaction (PPI) network obtained from the MINT database. The computational efficiency of the proposed algorithm outperforms most of the existing network motif discovery algorithms.

scholarly journals Identifying Representative Network Motifs for Inferring Higher-order Structure of Biological Networks

2018 ◽  
Author(s):  
Wang Tao ◽  
Yadong Wang ◽  
Jiajie Peng ◽  
Chen Jin
Keyword(s):  
Biological Networks ◽  
Motif Discovery ◽  
Network Motif ◽  
Low Frequency ◽  
Organizational Structures ◽  
Higher Order ◽  
Order Structure ◽  
Network Motifs ◽  
Scalable Algorithm ◽  
Significant Patterns

AbstractNetwork motifs are recurring significant patterns of inter-connections, which are recognized as fundamental units to study the higher-order organizations of networks. However, the principle of selecting representative network motifs for local motif based clustering remains largely unexplored. We present a scalable algorithm called FSM for network motif discovery. FSM accelerates the motif discovery process by effectively reducing the number of times to do subgraph isomorphism labeling. Multiple heuristic optimizations for subgraph enumeration and subgraph classification are also adopted in FSM to further improve its performance. Experimental results show that FSM is more efficient than the compared models on computational efficiency and memory usage. Furthermore, our experiments indicate that large and frequent network motifs may be more appropriate to be selected as the representative network motifs for discovering higher-order organizational structures in biological networks than small or low-frequency network motifs.

scholarly journals IndeCut evaluates performance of network motif discovery algorithms

2017 ◽  
Vol 34 (9) ◽  
pp. 1514-1521 ◽  
Author(s):  
Mitra Ansariola ◽  
Molly Megraw ◽  
David Koslicki
Keyword(s):  
Motif Discovery ◽  
Network Motif ◽  
Discovery Algorithms

scholarly journals MODA: An efficient algorithm for network motif discovery in biological networks

2009 ◽  
Vol 84 (5) ◽  
pp. 385-395 ◽  
Author(s):  
Saeed Omidi ◽  
Falk Schreiber ◽  
Ali Masoudi-Nejad
Keyword(s):  
Biological Networks ◽  
Efficient Algorithm ◽  
Motif Discovery ◽  
Network Motif

scholarly journals An Efficient Network Motif Discovery Approach for Co-Regulatory Networks

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 14151-14158 ◽  
Author(s):  
Jiawei Luo ◽  
Lv Ding ◽  
Cheng Liang ◽  
Nguyen Hoang Tu
Keyword(s):  
Regulatory Networks ◽  
Motif Discovery ◽  
Network Motif

scholarly journals Application of dynamic expansion tree for finding large network motifs in biological networks

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6917 ◽  
Author(s):  
Sabyasachi Patra ◽  
Anjali Mohapatra
Keyword(s):  
Biological Networks ◽  
Protein Function ◽  
Large Scale ◽  
Network Motif ◽  
Graph Isomorphism ◽  
Interaction Network ◽  
Motif Finding ◽  
Network Motifs ◽  
Large Network ◽  
Scalable Network

Network motifs play an important role in the structural analysis of biological networks. Identification of such network motifs leads to many important applications such as understanding the modularity and the large-scale structure of biological networks, classification of networks into super-families, and protein function annotation. However, identification of large network motifs is a challenging task as it involves the graph isomorphism problem. Although this problem has been studied extensively in the literature using different computational approaches, still there is a lot of scope for improvement. Motivated by the challenges involved in this field, an efficient and scalable network motif finding algorithm using a dynamic expansion tree is proposed. The novelty of the proposed algorithm is that it avoids computationally expensive graph isomorphism tests and overcomes the space limitation of the static expansion tree (SET) which makes it enable to find large motifs. In this algorithm, the embeddings corresponding to a child node of the expansion tree are obtained from the embeddings of a parent node, either by adding a vertex or by adding an edge. This process does not involve any graph isomorphism check. The time complexity of vertex addition and edge addition are O(n) and O(1), respectively. The growth of a dynamic expansion tree (DET) depends on the availability of patterns in the target network. Pruning of branches in the DET significantly reduces the space requirement of the SET. The proposed algorithm has been tested on a protein–protein interaction network obtained from the MINT database. The proposed algorithm is able to identify large network motifs faster than most of the existing motif finding algorithms.

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