Strategies for network motifs discovery

2012 IEEE 12th International Conference on Data Mining Workshops, 2012

Unexpectedly frequent subgraphs, known as motifs, can help in characterizing the structure of complex networks. Most of the existing methods for finding motifs are designed for unweighted networks, where only the existence of connection between nodes is considered, and not their strength or capacity. However, in many real world networks, edges contain more information than just simple node connectivity. In this paper, we propose a new method to incorporate edge weight information in motif mining. We think of a motif as a subgraph that contains unexpected information, and we define a new significance measurement to assess this subgraph exceptionality. The proposed metric embeds the weight distribution in subgraphs and it is based on weight entropy. We use the gtrie data structure to find instances of k-sized subgraphs and to calculate its significance score. Following a statistical approach, the random entropy of subgraphs is then calculated, avoiding the time consuming step of random network generation. The discrimination power of the derived motif profile by the proposed method is assessed against the results of the traditional unweighted motifs through a graph classification problem. We use a set of labeled ego networks of co-authorship in the biology and mathematics fields. The new proposed method is shown to be feasible, achieving even slightly better accuracy. Since it does not require the generation of random networks, it is also computationally faster, and because we are able to use the weight information in computing the motif importance, we can avoid converting weighted networks into unweighted ones.

Briefings in bioinformatics, 2014

Network motif detection is the search for statistically overrepresented subgraphs present in a larger target network. They are thought to represent key structure and control mechanisms. Although the problem is exponential in nature, several algorithms and tools have been developed for efficiently detecting network motifs. This work analyzes 11 network motif detection tools and algorithms. Detailed comparisons and insightful directions for using these tools and algorithms are discussed. Key aspects of network motif detection are investigated. Network motif types and common network motifs as well as their biological functions are discussed. Applications of network motifs are also presented. Finally, the challenges, future improvements and future research directions for network motif detection are also discussed.

dcc.fc.up.pt

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.

Bioinformatics and Biology Insights, 2015

The detection of network motifs has recently become an important part of network analysis across all disciplines. In this work, we detected and analyzed network motifs from undirected and directed networks of several different disciplines, including biological network, social network, ecological network, as well as other networks such as airlines, power grid, and co-purchase of political books networks. Our analysis revealed that undirected networks are similar at the basic three and four nodes, while the analysis of directed networks revealed the distinction between networks of different disciplines. The study showed that larger motifs contained the three-node motif as a subgraph. Topological analysis revealed that similar networks have similar small motifs, but as the motif size increases, differences arise. Pearson correlation coefficient showed strong positive relationship between some undirected networks but inverse relationship between some directed networks. The study suggests that the three-node motif is a building block of larger motifs. It also suggests that undirected networks share similar low-level structures. Moreover, similar networks share similar small motifs, but larger motifs define the unique structure of individuals. Pearson correlation coefficient suggests that protein structure networks, dolphin social network, and co-authorships in network science belong to a superfamily. In addition, yeast protein-protein interaction network, primary school contact network, Zachary's karate club network, and copurchase of political books network can be classified into a superfamily.

IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2014

Network motif algorithms have been a topic of research mainly after the 2002-seminal paper from Milo et al. [1], which provided motifs as a way to uncover the basic building blocks of most networks. Motifs have been mainly applied in Bioinformatics, regarding gene regulation networks. Motif detection is based on induced subgraph counting. This paper proposes an algorithm to count subgraphs of size k + 2 based on the set of induced subgraphs of size k. The general technique was applied to detect 3, 4 and 5-sized motifs in directed graphs. Such algorithms have time complexity O(a(G)m), O(m 2) and O(nm 2), respectively, where a(G) is the arboricity of G(V, E). The computational experiments in public datasets show that the proposed technique was one order of magnitude faster than Kavosh and FANMOD. When compared to NetMODE, acc-Motif had a slightly improved performance.

2011

Many natural structures can be naturally represented by complex networks. Discovering network motifs, which are overrepresented patterns of inter-connections, is a computationally hard task related to graph isomorphism. Sequential methods are hindered by an exponential execution time growth when we increase the size of motifs and networks.

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 $O(1)$ 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.

Physical Review X

Many real world networks contain a statistically surprising number of certain subgraphs, called network motifs. In the prevalent approach to motif analysis, network motifs are detected by comparing subgraph frequencies in the original network with a statistical null model. In this paper we propose an alternative approach to motif analysis where network motifs are defined to be connectivity patterns that occur in a subgraph cover that represents the network using minimal total information. A subgraph cover is defined to be a set of subgraphs such that every edge of the graph is contained in at least one of the subgraphs in the cover. Some recently introduced random graph models that can incorporate significant densities of motifs have natural formulations in terms of subgraph covers and the presented approach can be used to match networks with such models. To prove the practical value of our approach we also present a heuristic for the resulting NP-hard optimization problem and give results for several real world networks.

Data Mining and Knowledge Discovery, 2017

Network motif discovery is the problem of finding subgraphs of a network that occur more frequently than expected, according to some reasonable null hypothesis. Such subgraphs may indicate small scale interaction features in genomic inter-Responsible editor: Srinivasan Parthasarathy.