FuzAg: Fuzzy Agglomerative Community Detection by Exploring the Notion of Self-Membership

Biswas, Anupam; Biswas, Bhaskar

doi:10.1109/tfuzz.2018.2795569

Cited by 33 publications

(16 citation statements)

References 30 publications

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“…To test the performance of the F-SGCD algorithm, real-word networks, and artificial networks of different scales are used to carry out comparative experiments. Furthermore, we also applied the same networks in the competing algorithms, including CNM [26], SCORE [27], LPA [28], MOEA [29], SPOC [30] and FuzAg [31]. The experiment was carried out on a single computer with…”

Section: Resultsmentioning

confidence: 99%

A multi-objective adaptive evolutionary algorithm to extract communities in networks

Cao

Ding

et al. 2020

Swarm and Evolutionary Computation

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Community structure is one of the most important attributes of complex networks, which reveals the hidden rules and behavior characteristics of complex networks. Existing works need to pre-set weight parameters to control the different emphasis on the objective function, and cannot automatically identify the number of communities. In the process of optimization, there will be some challenges, such as premature and inefficiency. This paper presents a

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Section: Resultsmentioning

confidence: 99%

A multi-objective adaptive evolutionary algorithm to extract communities in networks

Cao

Ding

et al. 2020

Swarm and Evolutionary Computation

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“…Ann. Michael et al, [28] Hadoop Newman et al, [32] EM Guo-Jun et al, [16] EIMF Blondel et al, [4] Blondel et al Biswas et al, [6] FuzAg Clauset et al, [1] CNM Lyzinski et al, [26] HSBM Berry et al, [7] Berry et al Lyu et al, [25] FOX Ciglan et al, [8] Ciglan et al Palla et al, [35] Cfinder Lai et al, [22] Lai et al Xie et al, [20] SLPA DeMeo et al, [36] DeMeo et al…”

Section: Evaluation Setupmentioning

confidence: 99%

Detecting Disjoint Communities in a Social Network based on the Degrees of Association between Edges and Influential Nodes

Taha

2020

IEEE Trans. Knowl. Data Eng.

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Detecting communities is crucial to understanding the dynamics of their members. However, the detection of "good" communities is deemed demonstrably problematic, which is mainly due to the following two factors. First, real-world networks are complex and require optimizing multi-objective functions for capturing their community structures, whereas most current approaches optimize only one or two objective functions. Second, most current approaches detect communities in respect of the independence of how closely associated their connections are based on the global relative influences of the edges connecting them. To overcome these limitations, a clustering method needs to optimize multi-objective functions and employ global preprocessing techniques that consider the topology of the entire network. We, therefore, proposed a system called DAVE, which optimizes four objective functions that capture the community structures in most real-word network settings, and detects communities with regards of how closely associated their connections are based on the relative influences of the edges connecting them. We proposed novel formulas that capture these functions. Our method is the first to utilize the prediction of node-node associations based on global node-edge degrees of association. After ranking nodes based on their global relative influences on the network, some of the top-ranked ones will serve as core seeds for constructing communities. Then, the degrees of association between influential edges and seed nodes are computed. DAVE assigns a node to a community, only if each edge in the shortest path from this node to the community's core seed node is both influential and has significant degree of association with the core node. We evaluated DAVE by comparing it empirically and experimentally with sixteen methods. Results showed a remarkable improvement.

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“…However, these techniques cannot produce true membership values corresponding to nodes with high between-ness [36]. Therefore, many works discuss overlapping community detection [4,28,29,30,31,32,33,36,125,126,127,128,129,130,131,132,133,134]. Although some of these techniques have acceptable performance in terms of modularity, most of them suffer from high computational complexity and only are applied to small or medium size networks [4,31,33,36,125,126,127,128,133,134,135].…”

Section: Introductionmentioning

confidence: 99%

“…In [130] the authors proposed a game theory based method which is too complex for very large networks-on the order of hundreds of minutes for networks less than 10 5 nodes. In [134], the authors proposed a fuzzy agglomerative (FuzAg) approach for community detection that iteratively updates membership degree of nodes. The time complexity of the FuzAg algorithm is O(n 2 ).…”

Section: Introductionmentioning

confidence: 99%

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Community Detection in Complex Networks and Application to Dense Wireless Sensor Networks Localization

Yazdanparast¹

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FuzAg: Fuzzy Agglomerative Community Detection by Exploring the Notion of Self-Membership

Cited by 33 publications

References 30 publications

A multi-objective adaptive evolutionary algorithm to extract communities in networks

A multi-objective adaptive evolutionary algorithm to extract communities in networks

Detecting Disjoint Communities in a Social Network based on the Degrees of Association between Edges and Influential Nodes

Community Detection in Complex Networks and Application to Dense Wireless Sensor Networks Localization

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