Community detection based on modularity and k-plexes

Zhu, Jinrong; Chen, Bilian; Zeng, Yanjun

doi:10.1016/j.ins.2019.10.076

Cited by 68 publications

(17 citation statements)

References 37 publications

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“…In practice, the 𝑘-plex is closely related to the community detection problem which asks for dense and large communities from a large network [14,32]. Using maximal 𝑘-plex as a graph model of the community, we translate the community detection as listing maximal 𝑘-plexes that are at least connected, and with prescribed number of vertices.…”

Section: Some Propertiesmentioning

confidence: 99%

Listing Maximal k-Plexes in Large Real-World Graphs

Wang,

Zhou,

Xiao

et al. 2022

Preprint

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Listing dense subgraphs in large graphs plays a key task in varieties of network analysis applications like community detection. Clique, as the densest model, has been widely investigated. However, in practice, communities rarely form as cliques for various reasons, e.g., data noise. Therefore, 𝑘-plex, -graph with each vertex adjacent to all but at most 𝑘 vertices, is introduced as a relaxed version of clique. Often, to better simulate cohesive communities, an emphasis is placed on connected 𝑘-plexes with small 𝑘. In this paper, we continue the research line of listing all maximal 𝑘-plexes and maximal 𝑘-plexes of prescribed size. Our first contribution is algorithm ListPlex that lists all maximal 𝑘-plexes in 𝑂 * (𝛾 𝐷 ) time for each constant 𝑘, where 𝛾 is a value related to 𝑘 but strictly smaller than 2, and 𝐷 is the degeneracy of the graph that is far less than the vertex number 𝑛 in real-word graphs. Compared to the trivial bound of 2 𝑛 , the improvement is significant, and our bound is better than all previously known results. In practice, we further use several techniques to accelerate listing 𝑘-plexes of a given size, such as structural-based prune rules, cache-efficient data structures, and parallel techniques. All these together result in a very practical algorithm. Empirical results show that our approach outperforms the state-of-the-art solutions by up to orders of magnitude. CCS CONCEPTS• Information systems → Web mining; • Theory of computation → Graph algorithms analysis.

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Section: Some Propertiesmentioning

confidence: 99%

Listing Maximal k-Plexes in Large Real-World Graphs

Wang,

Zhou,

Xiao

et al. 2022

Preprint

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“…The sum of the connection strength of a vertex and its neighbors can be considered as the connection coefficient of this vertex. However, many community detection algorithms based on modularity methods usually have a resolution limitation that makes them unable to detect communities that are sufficiently smaller compared to the entire network [10,33]. To overcome the resolution limitation caused by the large gap in community size, we proposed to define the relative connection coefficient of each vertex based on the ratio of a vertex's connection coefficient to the sum of its neighbors' connection coefficients.…”

Section: Relative Connection Coefficientmentioning

confidence: 99%

Finding Central Vertices and Community Structure via Extended Density Peaks-Based Clustering

Meng

Liu

2021

Information

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Community detection is a significant research field of social networks, and modularity is a common method to measure the division of communities in social networks. Many classical algorithms obtain community partition by improving the modularity of the whole network. However, there is still a challenge in community division, which is that the traditional modularity optimization is difficult to avoid resolution limits. To a certain extent, the simple pursuit of improving modularity will cause the division to deviate from the real community structure. To overcome these defects, with the help of clustering ideas, we proposed a method to filter community centers by the relative connection coefficient between vertices, and we analyzed the community structure accordingly. We discuss how to define the relative connection coefficient between vertices, how to select the community centers, and how to divide the remaining vertices. Experiments on both real and synthetic networks demonstrated that our algorithm is effective compared with the state-of-the-art methods.

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“…Community detection can be informally considered as a problem of finding such communities in networks, which aims at assigning community labels to nodes such that the nodes in the same community share higher similarity than the nodes in different communities [ 49 ] [ 50 ]. Communities in networks are the division of networks into the groups of nodes having dense intraconnections and sparse interconnections [ 51 ]. In other words, the connections among nodes in communities are dense, while the connections between communities are sparse.…”

Section: Artificial Intelligence Modulementioning

confidence: 99%

A Semantic Analysis and Community Detection-Based Artificial Intelligence Model for Core Herb Discovery from the Literature: Taking Chronic Glomerulonephritis Treatment as a Case Study

Zhang

Liu

Zhu

et al. 2020

Computational and Mathematical Methods in Medicine

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The Traditional Chinese Medicine (TCM) formula is the main treatment method of TCM. A formula often contains multiple herbs where core herbs play a critical therapeutic effect for treating diseases. It is of great significance to find out the core herbs in formulae for providing evidences and references for the clinical application of Chinese herbs and formulae. In this paper, we propose a core herb discovery model CHDSC based on semantic analysis and community detection to discover the core herbs for treating a certain disease from large-scale literature, which includes three stages: corpus construction, herb network establishment, and core herb discovery. In CHDSC, two artificial intelligence modules are used, where the Chinese word embedding algorithm ESSP2VEC is designed to analyse the semantics of herbs in Chinese literature based on the stroke, structure, and pinyin features of Chinese characters, and the label propagation-based algorithm LILPA is adopted to detect herb communities and core herbs in the herbal semantic network constructed from large-scale literature. To validate the proposed model, we choose chronic glomerulonephritis (CGN) as an example, search 1126 articles about how to treat CGN in TCM from the China National Knowledge Infrastructure (CNKI), and apply CHDSC to analyse the collected literature. Experimental results reveal that CHDSC discovers three major herb communities and eighteen core herbs for treating different CGN syndromes with high accuracy. The community size, degree, and closeness centrality distributions of the herb network are analysed to mine the laws of core herbs. As a result, we can observe that core herbs mainly exist in the communities with more than 25 herbs. The degree and closeness centrality of core herb nodes concentrate on the range of [15, 40] and [0.25, 0.45], respectively. Thus, semantic analysis and community detection are helpful for mining effective core herbs for treating a certain disease from large-scale literature.

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Community detection based on modularity and k-plexes

Cited by 68 publications

References 37 publications

Listing Maximal k-Plexes in Large Real-World Graphs

Listing Maximal k-Plexes in Large Real-World Graphs

Finding Central Vertices and Community Structure via Extended Density Peaks-Based Clustering

A Semantic Analysis and Community Detection-Based Artificial Intelligence Model for Core Herb Discovery from the Literature: Taking Chronic Glomerulonephritis Treatment as a Case Study

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