Community Detection in Large-scale Bipartite Networks

Liu, Xin; Murata, Tsuyoshi

doi:10.1527/tjsai.25.16

Cited by 38 publications

(30 citation statements)

References 10 publications

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“…Bipartite networks between antagonists can be classified as random, nested, modular or one‐to‐one (e.g. Newman ; Liu & Murata ; Ulrich ; Weitz et al . ).…”

Section: Introductionmentioning

confidence: 99%

Network structure and local adaptation in co‐evolving bacteria–phage interactions

et al. 2017

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Numerous theoretical and experimental studies have investigated antagonistic co-evolution between parasites and their hosts. Although experimental tests of theory from a range of biological systems are largely concordant regarding the influence of several driving processes, we know little as to how mechanisms acting at the smallest scales (individual molecular and phenotypic changes) may result in the emergence of structures at larger scales, such as co-evolutionary dynamics and local adaptation. We capitalized on methods commonly employed in community ecology to quantify how the structure of community interaction matrices, so-called bipartite networks, reflected observed co-evolutionary dynamics, and how phages from these communities may or may not have adapted locally to their bacterial hosts. We found a consistent nested network structure for two phage types, one previously demonstrated to exhibit arms race co-evolutionary dynamics and the other fluctuating co-evolutionary dynamics. Both phages increased their host ranges through evolutionary time, but we found no evidence for a trade-off with impact on bacteria. Finally, only bacteria from the arms race phage showed local adaptation, and we provide preliminary evidence that these bacteria underwent (sometimes different) molecular changes in the wzy gene associated with the LPS receptor, while bacteria co-evolving with the fluctuating selection phage did not show local adaptation and had partial deletions of the pilF gene associated with type IV pili. We conclude that the structure of phage-bacteria interaction networks is not necessarily specific to co-evolutionary dynamics, and discuss hypotheses for why only one of the two phages was, nevertheless, locally adapted.

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“…Bipartite networks between antagonists can be classified as random, nested, modular or one‐to‐one (e.g. Newman ; Liu & Murata ; Ulrich ; Weitz et al . ).…”

Section: Introductionmentioning

confidence: 99%

Network structure and local adaptation in co‐evolving bacteria–phage interactions

et al. 2017

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“…LP BRIM Liu et al extended the work of BRIM to propose a joint method of label propagation (LP) and BRIM named LP BRIM [43]. Its time complexity is at most O(n 2 ) (where n is the number of vertices), which is acceptable to be applied in real networks.…”

Section: Settingsmentioning

confidence: 99%

A fast community detection method in bipartite networks by distance dynamics

Sun

Ch’ng²,

et al. 2018

Physica A: Statistical Mechanics and its Applications

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Many real bipartite networks are found to be divided into two-mode communities. In this paper, we formulate a new two-mode community detection algorithm BiAttractor. It is based on distance dynamics model Attractor proposed by Shao et al. with extension from unipartite to bipartite networks. Since Jaccard coefficient of distance dynamics model is incapable to measure distances of different types of vertices in bipartite networks, our main contribution is to extend distance dynamics model from unipartite to bipartite networks using a novel measure Local Jaccard Distance (LJD). Furthermore,

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“…LPBRIM [10] is a community detection algorithm that optimizes the bimodularity [20] which is an extension of the modularity for bipartite graphs. It relies on BRIM algorithm (Bipartite, Recursively Induced Modules) and uses a label propagation procedure.…”

Section: Lpbrimmentioning

confidence: 99%

“…In that regard, only few community detection methods have been proposed to take into account this inherent bipartite complexity of real networks [10,11,12]. The usual approach consists instead in projecting first the bipartite structure over one set of nodes and then applying standard community detection techniques.…”

Section: Introductionmentioning

confidence: 99%

ComSim: A Bipartite Community Detection Algorithm Using Cycle and Node’s Similarity

Tackx

Tarissan

Guillaume

2017

Complex Networks &Amp; Their Applications VI

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This study proposes COMSIM, a new algorithm to detect communities in bipartite networks. This approach generates a partition of nodes by relying on similarity between the nodes in terms of links towards ⊥ nodes. In order to show the relevance of this approach, we implemented and tested the algorithm on 2 small datasets equipped with a ground-truth partition of the nodes. It turns out that, compared to 3 baseline algorithms used in the context of bipartite graph, COMSIM proposes the best communities. In addition, we tested the algorithm on a large scale network. Results show that COMSIM has good performances, close in time to Louvain. Besides, a qualitative investigation of the communities detected by COMSIM reveals that it proposes more balanced communities.

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Community Detection in Large-scale Bipartite Networks

Cited by 38 publications

References 10 publications

Network structure and local adaptation in co‐evolving bacteria–phage interactions

Network structure and local adaptation in co‐evolving bacteria–phage interactions

A fast community detection method in bipartite networks by distance dynamics

ComSim: A Bipartite Community Detection Algorithm Using Cycle and Node’s Similarity

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