GLEAM: a graph clustering framework based on potential game optimization for large-scale social networks

Bu, Zhan; Li, Hui-Jia; Gao, Guangliang; Tao, Haicheng

doi:10.1007/s10115-017-1105-6

Cited by 53 publications

(10 citation statements)

References 40 publications

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“…Clustering techniques, as one of the main contents of data mining, are usually applied when there is no clear class to be predicted yet instances need to be divided into particular clusters. The data handled in this method are usually vector objects represented as attribute vectors [22] These clusters may reflect mechanisms that act in the domain from which the instances are extracted, which makes certain instances more similar to each other than to the rest [2]. After the clustering is completed, the different clusters are analyzed in detail.…”

Section: A Clustering Methods Of Data Miningmentioning

confidence: 99%

Mining Two-Line Element Data to Detect Orbital Maneuver for Satellite

et al. 2019

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Section: A Clustering Methods Of Data Miningmentioning

confidence: 99%

Mining Two-Line Element Data to Detect Orbital Maneuver for Satellite

et al. 2019

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“…The edge weight plays a very important role in detecting communities. Bu et al [8] proposed a very interesting method named GLEAM to identify communities based on game optimization. In their work, they employed cosine similarity to weight each edge including intra-edge and inter-edge.…”

Section: Strategies To Update Communitiesmentioning

confidence: 99%

“…Identifying user communities with similar interests can foster sharing resources; detecting consumer communities from online shopping sites is beneficial to target potential customers; discovering student communities from online education systems can promote the collaborative learning efficiency of students. Thus, it has received a great deal of attention [8,9,10]. Numerous techniques have been developed for community detection, such as partition based methods, hierarchical clustering methods, density based methods, modularity optimizing methods and game theory based methods.…”

Section: Introductionmentioning

confidence: 99%

An incremental method to detect communities in dynamic evolving social networks

Zhao

Zhang

et al. 2019

Knowledge-Based Systems

111

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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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GLEAM: a graph clustering framework based on potential game optimization for large-scale social networks

Cited by 53 publications

References 40 publications

Mining Two-Line Element Data to Detect Orbital Maneuver for Satellite

Mining Two-Line Element Data to Detect Orbital Maneuver for Satellite

An incremental method to detect communities in dynamic evolving social networks

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

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