acc-Motif: Accelerated Network Motif Detection (original) (raw)
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Kavosh: a new algorithm for finding network motifs
BMC …, 2009
Background: Complex networks are studied across many fields of science and are particularly important to understand biological processes. Motifs in networks are small connected sub-graphs that occur significantly in higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Existing algorithms for finding network motifs are extremely costly in CPU time and memory consumption and have practically restrictions on the size of motifs.
acc-MOTIF: Accelerated Motif Detection Using Combinatorial Techniques
2012
Background: Network motif algorithms have been a topic of research mainly after the 2002-seminal paper from Milo et al, that provided motifs as a way to uncover the basic building blocks of most networks. In bioinformatics, motifs has been mainly applied in gene regulation networks field. Results: This article proposes two new algorithms to exactly count isomorphic pattern motifs of size 3 and 4 in directed graphs. The algorithms are accelerated by combinatorial techniques. Let G(V, E) be a directed graph with m = |E|. We describe an O(m √ m) time complexity algorithm to count isomorphic patterns of size 3. To counting isomorphic patterns of size 4, we propose an O(m 2) algorithm. Conclusion: The new algorithms were implemented and compared with Fanmod motif detection tool. The experiments show that our algorithms are expressively faster than the other tools. We also let our tool to detect motifs available in the Internet.
Efficient Counting of Network Motifs
2010 IEEE 30th International Conference on Distributed Computing Systems Workshops, 2010
Counting network motifs has an important role in studying a wide range of complex networks. However, when the network size is large, as in the case of Internet Topology and WWW graphs counting the number of motifs becomes prohibitive. Devising efficient motif counting algorithms thus becomes an important goal.
Evaluation of subgraph searching algorithms detecting network motif in biological networks
Frontiers of Computer Science in China, 2009
Despite several algorithms for searching subgraphs in motif detection presented in the literature, no effort has been done for characterizing their performance till now. This paper presents a methodology to evaluate the performance of three algorithms: edge sampling algorithm (ESA), enumerate subgraphs (ESU) and randomly enumerate subgraphs (RAND-ESU). A series of experiments are performed to test sampling speed and sampling quality. The results show that RAND-ESU is more efficient and has less computational cost than other algorithms for large-size motif detection, and ESU has its own advantage in small-size motif detection.
Suffix Graph - An Efficient Approach for Network Motif Mining
Journal of Data Mining in Genomics & Proteomics, 2016
Network motif is a pattern of inter-connections occurring in complex network in numbers that are significantly higher than those in similar randomized network. The basic premise of finding network motifs lie in the ability to compute the frequency of the subgraphs. In order to discover network motif, one has to compute a subgraph census on the original network that calculates the frequency of all the subgraphs of certain type. Then there is a need to compute the frequency of a set of subgraphs on the randomized similar network. The bottleneck of the entire motif discovery process is therefore to compute the subgraph frequencies and this is the core computational problem. The proposed work is to present the Suffix-Graph, a data structure that store graphs efficiently and to design an algorithm to retrieve subgraph efficiently that detects network motifs and apply them to transcriptional interactions in Escherichia coli.
An Faster Network Motif Detection Tool
2018
Network motif provides a way to uncover the basic building blocks of most complex networks. This task usually demands high computer processing, specially for motif with 5 or more vertices. This paper presents an extended methodology with the following features: (i) search for motifs up to 6 vertices, (ii) multithread processing, and a (iii) new enumeration algorithm with lower complexity. The algorithm to compute motifs solve isomorphism in O(1)O(1)O(1) with the use of hash table. Concurrent threads evaluates distinct graphs. The enumeration algorithm has smaller computational complexity. The experiments shows better performance with respect to other methods available in literature, allowing bioinformatic researchers to efficiently identify motifs of size 3, 4, 5, and 6.
Network Motif Discovery Using Subgraph Enumeration and Symmetry-Breaking
Lecture Notes in Computer Science, 2007
The study of biological networks and network motifs can yield significant new insights into systems biology. Previous methods of discovering network motifs -network-centric subgraph enumeration and sampling -have been limited to motifs of 6 to 8 nodes, revealing only the smallest network components. New methods are necessary to identify larger network sub-structures and functional motifs.
Accelerated Motif Detection Using Combinatorial Techniques
Network motif algorithms have been a topic of research mainly after the 2002-seminal paper from Milo et al, that provided motifs as a way to uncover the basic building blocks of most networks. This article proposes new algorithms to exactly count isomorphic pattern motifs of size 3 and 4 in directed graphs. The algorithms are accelerated by combinatorial techniques. Let G(V, E) be a directed graph with m = |E|. We describe an O(m √ m) time complexity algorithm to count isomorphic patterns of size 3. To counting isomorphic patterns of size 4, we propose an O(m 2) algorithm. The new algorithms were implemented and compared with Fanmod motif detection tool. The experiments show that our algorithms are expressively faster than Fanmod. We also let our tool to detect motifs, the acc-MOTIF, available in the Internet.
Network motif algorithms have been a topic of research mainly after the 2002-seminal paper from Milo \emph{et al}, that provided motifs as a way to uncover the basic building blocks of most networks. In Bioinformatics, motifs have been mainly applied in the field of gene regulation networks. This paper proposes new algorithms to exactly count isomorphic pattern motifs of sizes 3, 4 and 5 in directed graphs. Let G(V,E)G(V,E)G(V,E) be a directed graph with m=∣E∣m=|E|m=∣E∣. We describe an O(msqrtm)O({m\sqrt{m}})O(msqrtm) time complexity algorithm to count isomorphic patterns of size 3. In order to count isomorphic patterns of size 4, we propose an O(m2)O(m^2)O(m2) algorithm. To count patterns with 5 vertices, the algorithm is O(m2n)O(m^2n)O(m2n). The new algorithms were implemented and compared with FANMOD and Kavosh motif detection tools. The experiments show that our algorithms are expressively faster than FANMOD and Kavosh's. We also let our motif-detecting tool available in the Internet.
G-tries: an efficient data structure for discovering network motifs
Proceedings of the 2010 ACM Symposium on …, 2010
In this paper we propose a novel specialized data structure that we call g-trie, designed to deal with collections of subgraphs. The main conceptual idea is akin to a prefix tree in the sense that we take advantage of common topology by constructing a multiway tree where the descendants of a node share a common substructure. We give algorithms to construct a g-trie, to list all stored subgraphs, and to find occurrences on another graph of the subgraphs stored in the g-trie. We evaluate the implementation of this structure and its associated algorithms on a set of representative benchmark biological networks in order to find network motifs. To assess the efficiency of our algorithms we compare their performance with other known network motif algorithms also implemented in the same common platform. Our results show that indeed, g-tries are a feasible, adequate and very efficient data structure for network motifs discovery, clearly outperforming previous algorithms and data structures.