A novel metric for community detection

Shang, Ke-ke; Small, Michael; Wang, Yan; Yin, Di; Li, Shu

doi:10.1209/0295-5075/129/68002

Cited by 15 publications

(10 citation statements)

References 39 publications

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“…where v is the node, c is the community, and N (v) is the set of the neighbors of v. S nc is essentially based on the principle that links within a community are more predictable than external links [23].…”

Section: A Similarity Indicesmentioning

confidence: 99%

Large-Scale Network Community Detection Using Similarity-Guided Merge and Refinement

Tunalı

2021

IEEE Access

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It is possible to extract valuable insights about the functional properties of a system by identifying and inspecting the community structure in the network that models the system. Community detection aims to extract these community structures from networks. Many community detection methods have been proposed that consider the problem from different perspectives. However, with the emergence of very large and complex networks from variety of domains, there has been a growing need for community detection methods that can operate at scale effectively and efficiently. Considering this, we propose a novel algorithm for large-scale community detection, based on two novel similarity indices we propose as well.In the first stage of our proposed algorithm, we generate candidate communities using a mechanism similar to information propagation very rapidly. Then, we merge small candidates that have fewer nodes than a calculated threshold with the larger ones using similarity between nodes and communities. Next, we engage a refinement operation on the candidates by moving all nodes to the candidates to which they are most similar using the same similarity index again. After that, we merge small communities with larger ones by using the similarity between communities until no gain in the modularity is obtained. Finally, in the last stage, we employ the same refinement operation as in the third stage. With an extensive experimentation on real-world and artificially-generated benchmark networks, we demonstrate and verify the performance and effectiveness of the proposed algorithm comparing it with the state-of-the-art methods. Experimental results indicate that our algorithm scales very well with growing size and complexity of networks. Besides, our algorithm outperforms most state-of-the-art community detection methods both in detection performance and computation time.

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Section: A Similarity Indicesmentioning

confidence: 99%

Large-Scale Network Community Detection Using Similarity-Guided Merge and Refinement

Tunalı

2021

IEEE Access

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“…Adopting this modern view, a new category of algorithms for community detection, termed as the seed set expansion process, has been gaining more and more attention. The idea is to start with finite seed sets and expand them into communities by adding/removing nodes to/from the set if a certain measure of the community is improved, such as modularity [2], conductance [3], outwardness [25], fitness [4], significance (OSLOM, [26]), or aggregative metric based on link prediction algorithms [5]. One important line of seed set expansion algorithms originate from the PageRank algorithm and expand the seed set based on a random walk process, as pioneered by the work of Andersen and Lang [3] and Andersen et al [9].…”

Section: A2 Seed Set Expansionmentioning

confidence: 99%

Self-falsifiable hierarchical detection of overlapping communities on social networks

Zhang

2020

New J. Phys.

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No community detection algorithm can be optimal for all possible networks, thus it is important to identify whether the algorithm is suitable for a given network. We propose a multi-step algorithmic solution scheme for overlapping community detection based on an advanced label propagation process, which imitates the community formation process on social networks. Our algorithm is parameter-free and is able to reveal the hierarchical order of communities in the graph. The unique property of our solution scheme is self-falsifiability; an automatic quality check of the results is conducted after the detection, and the fitness of the algorithm for the specific network is reported. Extensive experiments show that our algorithm is self-consistent, reliable on networks of a wide range of size and different sorts, and is more robust than existing algorithms on both sparse and large-scale social networks. Results further suggest that our solution scheme may uncover features of networks' intrinsic community structures.Community detection is a central topic in network science. Pioneered by works represented by Palla et al [1], in recent years more and more studies focus on the detection of overlapping communities as opposed to exhaustive communities, a division also regarded as soft-partitioning versus hard-partitioning. Besides relying on the optimization of certain metrics, e.g. modularity [2], conductance [3], fitness [4], or aggregative metric based on link prediction algorithms [5] etc, as the extensively-studied traditional approach for detecting exhaustive communities, for overlapping communities, a lot of new tools are designed based on various ideas, including link communities [6, 7], clique percolation [8], seed set expansion [3,[9][10][11][12], label propagation [13][14][15][16], local spectral clustering method [17], and methods based on statistical inference, such as Infomap [18], the stochastic block model (SBM,[19,20]; in particular, methods adopting the belief propagation algorithm [21,22]), and other generative models [23,24].Despite the success of different solution schemes on various application fronts, some weak points of existing community detection algorithms could be pinned down in practice, which we believe might be problematic in certain cases (see appendix A for a detailed discussion). These weaknesses include: (1) many solution schemes are over-parameterized, and in some cases the tuning of parameters depends largely on unwarranted heuristics;(2) many scalable methods based on the seed set expansion process [2-4, 9, 25-28] may lack well-designed seeding strategies [10,11,29] and often rely on ad-hoc strategies; (3) some algorithms that claim to be local, as opposed to methods based on an optimization over the entire graph, in fact still optimize on the community level and thus do not guarantee complete locality; (4) the number of communities in the graph is often predetermined in certain algorithms, which might not be a good treatment, despite its claimed advantage [30] and the possible determina...

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“…Link prediction can predict the undiscovered edges in complex networks to find the central node in the network and thus detect the community structure in complex networks [12]. New community detection metric is proposed based on the principle that internal links are more predictable than external links [13]. Community structures are also effective for link prediction [14].…”

Section: Introductionmentioning

confidence: 99%

Is Megamerger Better?–Based on the Link Prediction Perspective

Wang

2022

IEEE Access

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Link prediction provides insight into the evolutionary mechanisms of complex networks by predicting missing edges. Existing research has proposed many similarity algorithms based on local information, and some link prediction algorithms typically perform better in different networks. It is generally believed that a megamerger is beneficial. In the perspective of link prediction, merging the good-performing algorithms brings higher prediction accuracy. And the more times the experiment is executed, the higher the accuracy of link prediction. Therefore, this research proposes a new link prediction algorithm based on the theory of megamerger in management and the concept of partnership, and uses ten actual complex networks for experiments to test the above two hypotheses. The experimental results show that megamerger is not applicable to the link prediction algorithm. In addition, there is no positive correlation between the increasing the quantity of experiments and improving the accuracy of the experiments, so the above two hypotheses are rejected. Hence, this research presumes that megamerger of the comprehensive information of the network, such as the resource flow between nodes, the degree of common neighbor nodes, and partnership of nodes, does not improve the accuracy of link prediction. For a refined network with a small number of nodes and a short average path length, it is recommended that the quantity of experiments be set to only ten can achieve the required accuracy of link prediction.INDEX TERMS Link prediction, megamerger, complex network.

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A novel metric for community detection

Cited by 15 publications

References 39 publications

Large-Scale Network Community Detection Using Similarity-Guided Merge and Refinement

Large-Scale Network Community Detection Using Similarity-Guided Merge and Refinement

Self-falsifiable hierarchical detection of overlapping communities on social networks

Is Megamerger Better?–Based on the Link Prediction Perspective

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