Distributed Louvain Algorithm for Graph Community Detection

Ghosh, Sayan; Halappanavar, Mahantesh; Tumeo, Antonino; Kalyanaraman, Ananth; Lu, Hao; Chavarŕıa-Miranda, Daniel; Khan, Arif; Gebremedhin, Assefaw H.

doi:10.1109/ipdps.2018.00098

Cited by 84 publications

(30 citation statements)

References 21 publications

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“…12 Another method of clustering is known as spectral clustering. In this method, continuous learning of node features in the social network, and mapping nodes to 24 BAKHTHEMMAT AND IZADI other nodes are accomplished in low-dimensional space. This learning can calculate the similarity between two nodes.…”

Section: Clustering Methodsmentioning

confidence: 99%

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Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Bakhthemmat

Izadi

2021

Big Data

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Community detection in social networks is one of the advertising methods in electronic marketing. One of the approaches to find communities in large social networks is to use greedy methods, because these methods perform very fast. Greedy methods are generally designed based on local decisions; thus, inappropriate local decisions may result in an improper global solution. The use of a greedy improved index with a futuristic approach can, to some extent, prevent inappropriate local choices. Our proposed method determines the influential nodes in the social network based on the followers and following and new futuristic greedy index. It classifies the nodes based on the influential nodes by the density-based clustering algorithm with a new distance function. The proposed method can improve clustering precision to detect communities by the futuristic greedy approach. We implemented the proposed algorithm with the map-reduce technique in the Hadoop structure. Experimental results in datasets show that the average of the rand index of clusters was accomplished by 99.32% in the proposed method. In addition, these results illustrate that there is a reduction in execution time by the proposed algorithm.

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Section: Clustering Methodsmentioning

confidence: 99%

“…Once the maximum of modularity is obtained, the algorithm terminates. 24 Subsequently, many researchers used block modeling for improving greedy algorithms. The goal of block modeling is to reduce a big complex network to a smaller one.…”

Section: Clustering Methodsmentioning

confidence: 99%

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Bakhthemmat

Izadi

2021

Big Data

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“…In order to obtain the similarity among all of the enterprises, the listed company that appeared in the text was selected as the network node. We used the Louvain algorithm [37] to detect two clusters for the generated network. The range of the GNW generated node degree was 1-9, and the range of PMFG was 3-46, as shown in Figure 7.…”

Section: Distribution Of the Clustering Degreementioning

confidence: 99%

Research on the Knowledge Association Reasoning of Financial Reports Based on a Graph Network

2020

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With increasingly strict supervision, the complexity of enterprises’ annual reports has increased significantly, and the size of the text corpus has grown at an enormous rate. Information fusion for financial reporting has become a research hotspot. The difficulty of this problem is in filtering the massive amount of heterogeneous data and integrating related information distributed in different locations according to decision topics. This paper proposes a Graph NetWork (GNW) model that establishes the overall connection between decentralized information, as well as a graph network generation algorithm to filter large and complex data sets in financial reports and to mine key information to make it suitable for different decision situations. Finally, this paper uses the Planar Maximally Filtered Graph (PMFG) as a benchmark to show the effect of the generation algorithm.

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“…Authors [26] presented new algorithm that uses Louvain as a base and applies a distributed memory concept for detecting a community. They also presented several heuristics.…”

Section: Fig 1 Community Structurementioning

confidence: 99%

Modularity based Community Detection in Social Networks

Meena¹,

Tokekar²

2020

IJEAT

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 Abstract: The community detection is an interesting and highly focused area in the analysis of complex networks (CNA). It identifies closely connected clusters of nodes. In recent years, several approaches have been proposed for community detection and validation of the result. Community detection approaches that use modularity as a measure have given much weight-age by the research community. Various modularity based community detection approaches are given for different domains. The network in the real-world may be weighted, heterogeneous or dynamic. So, it is inappropriate to apply the same algorithm for all types of networks because it may generate incorrect result. Here, literature in the area of community detection and the result evaluation has been extended with an aim to identify various shortcomings. We think that the contribution of facts given in this paper can be very useful for further research.

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Distributed Louvain Algorithm for Graph Community Detection

Cited by 84 publications

References 21 publications

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Research on the Knowledge Association Reasoning of Financial Reports Based on a Graph Network

Modularity based Community Detection in Social Networks

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