Node similarity and modularity for finding communities in networks

Saoud, Bilal; Moussaoui, Abdelouahab

doi:10.1016/j.physa.2017.11.110

Cited by 36 publications

(12 citation statements)

References 23 publications

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“…The node similarity measure is used to compute the level of relationship between nodes. This measure is equally used to ascertain if nodes can be grouped together into the same community [ 1 , 3 , 16 ]. We determine the similarity between nodes via the structural similarity, which computes the intersections between the neighbourhood sets of any two nodes.…”

Section: Methodsmentioning

confidence: 99%

“…In recent years, existing community detection algorithms reported in the literature were specifically designed to either detect only disjoint nodes or overlapping nodes. Disjoint nodes, also known as node clusters, are nonoverlapping groups of densely connected subgraphs of a network [ 1 , 12 , 14 , 15 , 16 , 17 , 18 ]. Overlapping nodes are nodes shared by two or more communities at the same time, thereby creating overlapping communities [ 1 , 14 , 15 , 16 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

“…Disjoint nodes, also known as node clusters, are nonoverlapping groups of densely connected subgraphs of a network [1,12,[14][15][16][17][18]. Overlapping nodes are nodes shared by two or more communities at the same time, thereby creating overlapping communities [1, [14][15][16][19][20][21][22]. Previous methods rarely take into consideration disjunct nodes (isolated or neutral nodes) [23].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Type Node Detection in Network Communities

Ezeh

Ren

et al. 2019

Entropy

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Patterns of connectivity among nodes on networks can be revealed by community detection algorithms. The great significance of communities in the study of clustering patterns of nodes in different systems has led to the development of various methods for identifying different node types on diverse complex systems. However, most of the existing methods identify only either disjoint nodes or overlapping nodes. Many of these methods rarely identify disjunct nodes, even though they could play significant roles on networks. In this paper, a new method, which distinctly identifies disjoint nodes (node clusters), disjunct nodes (single node partitions) and overlapping nodes (nodes binding overlapping communities), is proposed. The approach, which differs from existing methods, involves iterative computation of bridging centrality to determine nodes with the highest bridging centrality value. Additionally, node similarity is computed between the bridge-node and its neighbours, and the neighbours with the least node similarity values are disconnected. This process is sustained until a stoppage criterion condition is met. Bridging centrality metric and Jaccard similarity coefficient are employed to identify bridge-nodes (nodes at cut points) and the level of similarity between the bridge-nodes and their direct neighbours respectively. Properties that characterise disjunct nodes are equally highlighted. Extensive experiments are conducted with artificial networks and real-world datasets and the results obtained demonstrate efficiency of the proposed method in distinctly detecting and classifying multi-type nodes in network communities. This method can be applied to vast areas such as examination of cell interactions and drug designs, disease control in epidemics, dislodging organised crime gangs and drug courier networks, etc.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Type Node Detection in Network Communities

Ezeh

Ren

et al. 2019

Entropy

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“…Table 6 presents a preliminary performance comparison of these algorithms in terms of detected communities and the corresponding modularity Q. For karate club network, ASOCCA obtains two unique connected component sets: set1 = { (24,25,26,27,15,21,23,33,32,31,16,28,29,34,19,30,9), (11,10,13,12,20,14,22,18,1,2,3,4,5,6,7,8,17)} set2 = { (24,10,25,26,27,15,21,23,33,32,31,16,28,29,34,19,…”

Section: ) Modularity Metrics Analysis Of Small and Medium Real Netwmentioning

confidence: 99%

A Novel Community Detection Algorithm Based on Local Similarity of Clustering Coefficient in Social Networks

Pan

Wang

et al. 2019

IEEE Access

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Community structures are integral and independent parts in a network. Community detection plays an important role in social networks for understanding the structure and predicting user behaviors. Many algorithms have been devised for accurate and efficient community detecting, but there are few community detection algorithms using node similarity. In most real-world networks, nodes tend to create tightly knit groups characterized by a relatively high density of ties. The higher the clustering coefficient of a node, the more aggregative the neighboring nodes are. In this paper, we propose an adjacent node similarity optimization combination connectivity algorithm (ASOCCA) for accurate community detection. ASOCCA utilizes the local similarity measure based on clustering coefficient to identify the closest neighbors of each node, then obtains several sets of connected components by combining different pairs of nodes, and finally forms initial communities. In addition, the community merging strategy is applied to further optimize the community structure. To evaluate the performance of the proposed algorithm, six real-world networks and two LFR networks with diverse network size are used to compare ASOCCA with five state-of-the-art community detection algorithms. The experimental results show that ASOCCA achieves better detection accuracy than several existing algorithms.

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“…Communities also indicate functional entities in a network. For example, in protein-protein interaction networks, communities represent groups of proteins with a specific function [6,31]; in social networks, communities are groups of people with similar interests or features [20,13]; in product co-purchasing networks (e.g., Amazon and eBay), communities correspond to the categories of products. Moreover, communities are useful in recommendation systems (e.g., location, music and film recommendation), i.e., Feng et al [9] adopted community detection technique to form collaborative recommendations since members in the same community share similar interests.…”

Section: Introductionmentioning

confidence: 99%

Community detection based on modularity and k-plexes

Zhu

Chen

Zeng

2020

Information Sciences

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Community identification is of great worth for analyzing the structure or characteristics of a complex network. Many community detection methods have been developed, such as modularity-based optimization models, which are widely used but significantly restricted in "resolution limit". In this paper, we propose a novel algorithm, called modularity optimization with k-plexes (MOKP), to solve this problem, and this algorithm can identify communities smaller than a scale. The proposed algorithm uses k-plexes to generate community seeds from the whole network and assigns the remaining nodes by modularity optimization. To save computational time, we further propose the improved MOKP algorithm (IMOKP) by reducing the scale of the network before community seeds generation and adjusting rules of nodes assignment. Extensive experimental results demonstrate our proposed algorithms perform better than several state-of-the-art algorithms in terms of accuracy of detected communities on various networks, and can effectively detect small communities in terms of a newly defined index, namely small community level, on multiple networks as well.

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Node similarity and modularity for finding communities in networks

Cited by 36 publications

References 23 publications

Multi-Type Node Detection in Network Communities

Multi-Type Node Detection in Network Communities

A Novel Community Detection Algorithm Based on Local Similarity of Clustering Coefficient in Social Networks

Community detection based on modularity and k-plexes

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