A Graph Clustering Algorithm Using Attraction-Force Similarity for Community Detection

Zhou, Huanfu; Xi, Bingyan; Zhang, Yihui; Li, Junhuai; Zhang, Facun

doi:10.1109/access.2018.2889312

Cited by 6 publications

(4 citation statements)

References 31 publications

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“…Labels to form communities group the nodes. 4,21 In crowd-based greedy methods, node communication is used to form communities. These methods are evaluated by using the concept of modularity of constructed communities.…”

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%

“…In this model, the clustering process uses the k-means algorithm. 21 Innovative methods are presented in recent years as link prediction methods. Link prediction methods anticipate the likelihood of a future connection between two nodes in a given network.…”

Section: Communities Detection By Futuristic Approachmentioning

confidence: 99%

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Bakhthemmat

Izadi

2021

Big Data

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“…The distance between each triangle and the nodes based on the two categories are calculated respectively. And then arranged according to the ascending order, the halo nodes are assigned to a similar class by determining the number of connections between the most recent nodes by selecting the K value [34]. As we can see from the Fig.…”

Section: B a Novel Knn-hdpc Based On The Knn Theorymentioning

confidence: 99%

A Novel Density Peaks Clustering Halo Node Assignment Method Based on K-Nearest Neighbor Theory

et al. 2019

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The density peaks clustering (DPC) algorithm is not sensitive to the recognition of halo nodes. The halo nodes at the edge of the density peaks clustering algorithm has a lower local density. The outliers are distributed in halo nodes. The novel halo identification method based on density peaks clustering algorithm utilize the advantage of DBSCAN algorithm to quickly identify outliers, which improved the sensitivity to halo nodes. However, the identified halo nodes cannot be effectively assigned to adjacent clusters. Therefore, this paper will use K-nearest neighbor (KNN) algorithm to classify the identified halo nodes. K-nearest neighbor is the simplest and most efficient classification method. The KNN algorithm has the advantages of high accuracy, insensitivity to outliers and no input hypothesis data. Hence, we proposed a novel density peaks clustering halo node assignment algorithm based on K-nearest neighbor theory (KNN-HDPC). KNN-HDPC can grasp the internal relations between outliers and cluster nodes more deeply, so as to dig out the deeper relations between nodes. Experimental results demonstrate that the proposed algorithm can effectively cluster and reclassify a large number of complex data. We can quickly dig out the potential relationship between noise points and cluster points. The improved algorithm has higher clustering accuracy than the original DPC algorithm, and essentially has more robust clustering results. INDEX TERMS Density peaks clustering, halo node, K-nearest neighbor, low-density nodes.

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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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A Graph Clustering Algorithm Using Attraction-Force Similarity for Community Detection

Cited by 6 publications

References 31 publications

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

A Novel Density Peaks Clustering Halo Node Assignment Method Based on K-Nearest Neighbor Theory

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

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