Subgraph Covers: An Information-Theoretic Approach to Motif Analysis in Networks

Wegner, Anatol E.

doi:10.1103/physrevx.4.041026

Cited by 14 publications

(16 citation statements)

References 20 publications

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“…For instance, in graph theory, it is customary to look for a minimal set of cliques covering the network [23,24]. Other methods appeal to notions of randomness and generative modeling to regularize the problem [1,25]. These methods describe an explicit process by which one goes from higher-order data to networks, and can therefore assign a likelihood to possible higher-order data representations.…”

Section: Us National Longitudinal Study Of Adolescent To Adultmentioning

confidence: 99%

Hypergraph reconstruction from network data

Young,

Petri,

Peixoto

2020

Preprint

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Networks can describe the structure of a wide variety of complex systems by specifying how pairs of nodes interact. This choice of representation is flexible, but not necessarily appropriate when joint interactions between groups of nodes are needed to explain empirical phenomena. Networks remain the de facto standard, however, as relational datasets often fail to include higher-order interactions. Here, we introduce a Bayesian approach to reconstruct these missing higher-order interactions, from pairwise network data. Our method is based on the principle of parsimony and only includes higher-order structures when there is sufficient statistical evidence for them.

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Section: Us National Longitudinal Study Of Adolescent To Adultmentioning

confidence: 99%

Hypergraph reconstruction from network data

Young,

Petri,

Peixoto

2020

Preprint

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“…They notice that large networks have very different structures. For example, different transcription networks from Escherichia coli and Saccharomyces cerevisiae have large differences in the frequency motif structures (Wegner, 2014). Over the past years, a number of graph clustering methods have been investigated.…”

Section: Related Workmentioning

confidence: 99%

“…Some local graph clustering methods have been investigated by incorporating high-order network information captured by small subgraphs (Yin et al, 2017;Lee, Gharan & Trevisan, 2014;Li et al, 2017b). In Wegner (2014), the authors define a subgraph cover to represent the network with motifs. The cover consists of a set of motifs and their corresponding frequencies.…”

Section: Related Workmentioning

confidence: 99%

“…Besides, the network motifs can be detected by comparing the frequencies of subgraphs in the original network with a statistical model. The authors in Wegner (2014) notice that real networks contain significant densities of different motifs. It illustrates networks in different fields hold different collaboration patterns, and motifs are the fingerprints of different networks.…”

Section: Related Workmentioning

confidence: 99%

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COMICS: a community property-based triangle motif clustering scheme

Feng

Zhang

et al. 2019

PeerJ Computer Science

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With the development of science and technology, network scales of various fields have experienced an amazing growth. Networks in the fields of biology, economics and society contain rich hidden information of human beings in the form of connectivity structures. Network analysis is generally modeled as network partition and community detection problems. In this paper, we construct a community property-based triangle motif clustering scheme (COMICS) containing a series of high efficient graph partition procedures and triangle motif-based clustering techniques. In COMICS, four network cutting conditions are considered based on the network connectivity. We first divide the large-scale networks into many dense subgraphs under the cutting conditions before leveraging triangle motifs to refine and specify the partition results. To demonstrate the superiority of our method, we implement the experiments on three large-scale networks, including two co-authorship networks (the American Physical Society (APS) and the Microsoft Academic Graph (MAG)), and two social networks (Facebook and gemsec-Deezer networks). We then use two clustering metrics, compactness and separation, to illustrate the accuracy and runtime of clustering results. A case study is further carried out on APS and MAG data sets, in which we construct a connection between network structures and statistical data with triangle motifs. Results show that our method outperforms others in both runtime and accuracy, and the triangle motif structures can bridge network structures and statistical data in the academic collaboration area. massive scale related data, such as digital libraries and scholarly networks . As collaboration behaviors among scholars are becoming frequent, collaboration networks are generally in large-scale and contain rich collaboration information, reflecting the cooperation patterns among scholars in different research areas. Bordons et al. (1996) regard the academic teams as scientists communities, in which scholars can share research methods, materials, and financial resources rather than institutions organized by fixed structures (Barjak & Robinson, 2008). Furthermore, the ternary closures in social networks constitute a minimal stable structure; that is, a loop with three nodes. The number of ternary closures in social networks changes over time, which reveals the evolvement of human social behaviors. Besides, the definition of a clustering coefficient is based on the distributions of ternary closures. Milo et al. (2002) defined small network structures as motifs to present interconnections in complex networks by numbers that are significantly higher than those in randomized networks. Motifs can define universal classes of networks, and researchers are carrying on the motif detection experiments on networks from different areas, such as biochemistry, neurobiology, and engineering, to uncover the existence of motifs and the corresponding structure information in networks (Ribeiro, Silva & Kaiser, 2009;Bian & Zhang, 2016). Hence, triangle mo...

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“…They notice that large networks have very different structures. For example, different transcription networks from E.coli and S.cerevisiae have large differences in the frequency motif structures Wegner (2014). Over the past years, a number of graph clustering methods have been…”

Section: Related Workmentioning

confidence: 99%

Peer Review #1 of "COMICS: a community property-based triangle motif clustering scheme (v0.2)"

2019

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With the development of science and technology, network scales of various fields have increased in an amazing growth. Networks in the fields of biology, economics and society contain rich hidden information of human beings in the form of connectivity structures. Network analysis is generally modeled as network partition and community detection problems. In this paper, we construct a community property-based triangle motif clustering scheme (COMICS), containing a series of high efficient graph partition procedures and triangle motif-based clustering techniques. In COMICS, four network cutting $conditions$ are considered based on the network connectivity. We first divide the large-scale networks into many dense subgraphs under the cutting conditions before leveraging triangle motifs to refine and specify the partition results. To demonstrate the superiority of our method, we implement the experiments on three large-scale networks, including two co-authorship networks, i.e., American physical Society (APS) and Microsoft Academic Graph (MAG), and two social networks, i.e., Facebook and gemsec-Deezer networks. We then use two clustering metrics: compactness and separation, to illustrate the accuracy and runtime of clustering results. A case study is further carried on APS and MAG data sets, in which we construct a connection between network structures and statistical data with triangle motifs. Results show that our method outperforms others in both runtime and accuracy, and the triangle motif structures can bridge network structures and statistical data in the academic collaboration area.

show abstract

Subgraph Covers: An Information-Theoretic Approach to Motif Analysis in Networks

Cited by 14 publications

References 20 publications

Hypergraph reconstruction from network data

Hypergraph reconstruction from network data

COMICS: a community property-based triangle motif clustering scheme

Peer Review #1 of "COMICS: a community property-based triangle motif clustering scheme (v0.2)"

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