Metrics Matter in Community Detection

McCarthy, Arya D.; Chen, Tongfei; Rudinger, Rachel; Matula, David W.

doi:10.1007/978-3-030-36687-2_14

Cited by 6 publications

(5 citation statements)

References 35 publications

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“…Adjusted Mutual Information (AMI) [ 60 , 61 ] is an adjustment of the Mutual Information (MI) score to account for chance. AMI augments NMI’s consistent upper bound (1.0) with a consistent zero expectation to adjust for chance clusterings.…”

Section: Resultsmentioning

confidence: 99%

LPA-MNI: An Improved Label Propagation Algorithm Based on Modularity and Node Importance for Community Detection

Zhang

Zhao

et al. 2021

Entropy

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Community detection is of great significance in understanding the structure of the network. Label propagation algorithm (LPA) is a classical and effective method, but it has the problems of randomness and instability. An improved label propagation algorithm named LPA-MNI is proposed in this study by combining the modularity function and node importance with the original LPA. LPA-MNI first identify the initial communities according to the value of modularity. Subsequently, the label propagation is used to cluster the remaining nodes that have not been assigned to initial communities. Meanwhile, node importance is used to improve the node order of label updating and the mechanism of label selecting when multiple labels are contained by the maximum number of nodes. Extensive experiments are performed on twelve real-world networks and eight groups of synthetic networks, and the results show that LPA-MNI has better accuracy, higher modularity, and more reasonable community numbers when compared with other six algorithms. In addition, LPA-MNI is shown to be more robust than the traditional LPA algorithm.

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Section: Resultsmentioning

confidence: 99%

LPA-MNI: An Improved Label Propagation Algorithm Based on Modularity and Node Importance for Community Detection

Zhang

Zhao

et al. 2021

Entropy

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“…NMI is a useful information measure in information theory. It is introduced by Leon Danon et al [35] and used to measure the similarity between the detected communities and the known communities. Given two partitions A and B of a network in communities, let C be the confusion matrix whose element C ij is the number of nodes of community i of the partition A that are also in the community j of the partition B.…”

Section: Evaluation Metricsmentioning

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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“…Community mining algorithms are mainly divided into two categories: agglomeration [34], [35] and decomposition [36]. The basic idea of agglomeration algorithm is to add links for nodes with higher similarity.…”

Section: Construction Of the Community Structure Of Power Communmentioning

confidence: 99%

An Effective Reliability Evaluation Method for Power Communication Network Based on Community Structure

Cao

Tanveer

et al. 2020

IEEE Trans. on Ind. Applicat.

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The reliability evaluation of power communication network is beneficial for the improvement of stable operation of the power system and the robustness of the power grid. However, the existing reliability evaluation models of the power communication network cannot meet the current situation of timeliness performance, due to rapidly increasing scale and complexity of information across varying services. In this study, we used the complex network theory to analyze the structure of power communication network and constructed the evaluation index of node (link) reliability of power communication network based on community reliability. Compared with the traditional reliability indexes, our index not only considers the influence of the environment of the node (link) on the single structure of the power communication network, but also possess the reliability evaluation rate of the node (link), which have the opportunities for improving the performance of the reliability evaluation of the wide-area power communication network. To verify the rationality of the index, we developed random, low reliability and high-betweenness deliberate attacks to attack the designated node (link), and compared the network efficiency before and after the attack. Based on the simulation results, it can verify the rationality and superiority of our proposed evaluation index.

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Metrics Matter in Community Detection

Cited by 6 publications

References 35 publications

LPA-MNI: An Improved Label Propagation Algorithm Based on Modularity and Node Importance for Community Detection

LPA-MNI: An Improved Label Propagation Algorithm Based on Modularity and Node Importance for Community Detection

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

An Effective Reliability Evaluation Method for Power Communication Network Based on Community Structure

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