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

Pan, Xiaohui; Xu, Gening; Wang, Bing; Zhang, Tao

doi:10.1109/access.2019.2937580

Cited by 21 publications

(9 citation statements)

References 48 publications

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“…ASOCCA (Adjacent node Similarity Optimization Combination Connectivity Algorithm) [37] is a combination connectivity algorithm for optimizing the similarity of adjacent nodes to achieve accurate community testing. It uses the local similarity measure based on the clustering coefficient to identify the nearest neighbor of each node.…”

Section: Methodsmentioning

confidence: 99%

Community Detection Based on Fish School Effect

Sun

Chang

et al. 2022

IEEE Access

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Community structure has a more intuitive, physical meaning than how it has traditionally been perceived; this representation plays an important role in networks and is accordingly becoming widely adapted. Consequently, community detection has attracted increasing attention. Although scholars have proposed many community detection methods from different perspectives, due to the complexity, diversity and dynamic characteristics of networks, efficient community detection in many real networks remains a challenge. Inspired by fish school effect in real life, this paper envisions networks as an ecosystem and proposes a novel dynamic model that aims to reveal the communities in a more intuitive way. Relying on the new model, we design a community detection algorithm, known as community detection based on fish school effect (CDFSE). CDFSE has plentiful desirable properties: high-quality community detection, parameters free and notable scalability. To evaluate the performance of CDFSE, this paper employs two widely utilized evaluation metrics and eleven representative algorithms to test the effectiveness of the algorithm in both synthetic and real-world networks. The experimental results show that in most cases, CDFSE is superior to the comparison methods in terms of the quality of community detection.

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Section: Methodsmentioning

confidence: 99%

Community Detection Based on Fish School Effect

Sun

Chang

et al. 2022

IEEE Access

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“…As shown in Figure 4 , X i and Y i are the standardized cash-to-assets ratio and current-liability ratio, respectively. After comparing the financial situation of the two types in the later period, we can find that the average financial performance of B -class companies is better than that of A -class companies [ 18 , 19 ]. However, the K-means algorithm also has some shortcomings.…”

Section: Methodsmentioning

confidence: 99%

Identification of Enterprise Financial Risk Based on Clustering Algorithm

Tao

2022

Computational Intelligence and Neuroscience

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In order to solve the problem that corporate financial risks seriously affect the healthy development of enterprises, credit institutions, securities investors, and even the whole of China, the K-means clustering algorithm, the risk screening process, and the Gaussian mixture clustering algorithm, the risk screening process, are proposed; experiments have shown that although the number of high-risk companies selected by the K-means algorithm is small, only 9% of the full sample, the high-risk cluster can contain nearly 30% of the new “special treatment” companies. If the time period is extended to the next 5 years, this proportion will be higher. Finally we found that if the prediction of “special handling” events is used as the criterion for evaluating high-risk clusters, then K-means clustering can effectively screen out those risky companies that need to be treated with caution by investors. The validity of the experiment is verified.

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“…Besides, the user needs to inform previously existing communities and the number of topics (subjects) to be considered by the model, which is not always feasible in practice. In [26], the authors proposed a community detection algorithm called "Adjacent node Similarity Optimization Combination Connectivity Algorithm (ASOCCA)", that considers the similarities amongst nodes by measuring the clustering coefficient of each node in the given network. Similar to the previous work, their algorithm is mainly designed to detect non-overlapping communities in an unweighted and undirected network.…”

Section: A Community Identification Methodsmentioning

confidence: 99%

TOSNet: A Topic-Based Optimal Subnetwork Identification in Academic Networks

et al. 2020

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Subnetwork identification plays a significant role in analyzing, managing, and comprehending the structure and functions in big networks. Numerous approaches have been proposed to solve the problem of subnetwork identification as well as community detection. Most of the methods focus on detecting communities by considering node attributes, edge information, or both. This study focuses on discovering subnetworks containing researchers with similar or related areas of interest or research topics. A topic-aware subnetwork identification is essential to discover potential researchers on particular research topics and provide quality work. Thus, we propose a topic-based optimal subnetwork identification approach (TOSNet). Based on some fundamental characteristics, this paper addresses the following problems: 1) How to discover topic-based subnetworks with a vigorous collaboration intensity? 2) How to rank the discovered subnetworks and single out one optimal subnetwork? We evaluate the performance of the proposed method against baseline methods by adopting the modularity measure, assess the accuracy based on the size of the identified subnetworks, and check the scalability for different sizes of benchmark networks. The experimental findings indicate that our approach shows excellent performance in identifying contextual subnetworks that maintain intensive collaboration amongst researchers for a particular research topic.

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

Cited by 21 publications

References 48 publications

Community Detection Based on Fish School Effect

Community Detection Based on Fish School Effect

Identification of Enterprise Financial Risk Based on Clustering Algorithm

TOSNet: A Topic-Based Optimal Subnetwork Identification in Academic Networks

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