Community detection in complex networks using density-based clustering algorithm and manifold learning

You, Tao; Cheng, Hui; Ning, Yi Zi; Shia, Ben‐Chang; Zhang, Zhong Yuan

doi:10.1016/j.physa.2016.07.025

Cited by 43 publications

(22 citation statements)

References 40 publications

(64 reference statements)

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“…According to the theoretical analysis of the resolution limit, a multi-resolution method based on asymptotic surprise was introduced, which is a generalization of asymptotic surprise to multi-scale networks. Moreover, to optimize 14 asymptotic surprise more effectively, we proposed an improved Louvain algorithm by using an effective initialization process and a refining process.…”

Section: Resultsmentioning

confidence: 99%

Identifying multi-scale communities in networks by asymptotic surprise

Zhang

et al. 2019

J. Stat. Mech.

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Optimizing statistical measures for community structure is one of the most popular strategies for community detection, but many of them lack the flexibility of resolution and thus are incompatible with multi-scale communities of networks. Here, we further studied a statistical measure of interest for community detection, asymptotic surprise, an asymptotic approximation of surprise. We discussed the critical behaviors of asymptotic surprise in phase transition of community partition theoretically. Then, according to the theoretical analysis, a multi-resolution method based on asymptotic surprise was introduced, which provides an alternative approach to study multi-scale communities in networks, and an improved Louvain algorithm was proposed to optimize the asymptotic surprise more effectively. By a series of experimental tests in various networks, we validated the critical behaviors of the asymptotic surprise further and the effectiveness of the improved Louvain algorithm, displayed its ability to solve the first-type resolution limit and stronger tolerance against the second-type resolution limit, and confirmed its effectiveness of revealing multi-scale community structures in multi-scale networks.

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Section: Resultsmentioning

confidence: 99%

Identifying multi-scale communities in networks by asymptotic surprise

Zhang

et al. 2019

J. Stat. Mech.

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“…The output of each algorithm was evaluated by four metrics, namely, extended module degree (EMD) [26] and extended partition density (EPD) [27], aiming to reflect the reasonability and quality of community detection. Figures 6 and 7 compares the EMDs and EPDs of our algorithm, the OSLOM, Fast-Newman, and the MCL on realworld networks, respectively.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

A Markov Chain-Based Overlapping Community Detection Algorithm for Complex Networks

Xing¹,

Liu²

2019

ISI

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Most community detection algorithms for complex networks are focused on nonoverlapping communities. However, there are many overlapping communities in real-world complex networks. To solve the contradiction, this paper develops a novel overlapping community detection algorithm based on Markov chain. First, the input adjacency matrix was expanded to guide the information flow. Then, the inflation operation was implemented to enhance the weakening boundary of communities. After that, an adaptive threshold was introduced to reconstruct the matrix. The network corresponding to the reconstructed matrix displays the overlapping communities in the original network. The proposed algorithm was compared with several popular community detection algorithms on artificial and real-world networks. The results show that our algorithm achieved higher recognition accuracy and faster convergence than the contrastive algorithms.

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“…Another kind of density-based methods is based on the density peak clustering algorithm, Fdp [27]. For instance, IsoFdp [28] maps vertices in the network as points in a lowdimensional manifold and then gets communities by clustering through Fdp. LCCD [29] exploits Fdp to identify the structural centers from the network and then acquires the results by expanding communities from the center vertices to the borders using a local search approach.…”

Section: Related Workmentioning

confidence: 99%

“…e seedexpanding methods are a typical kind of local approaches, which identify seeds of communities first utilizing various centrality indexes and then expand the communities by absorbing vertices to join according to some rules. For instance, the aforementioned IsoFdp [28] identifies seed vertices by finding points which are vertices mapped into a low-dimensional manifold with density peaks and then assigns other vertices to the nearest seeds. RWA [31] uses the degree centrality to select seed vertices from the network; it takes local maximum degree vertices as seeds of communities and then expands each of communities by adding the vertex which is most likely to belong to it repeatedly.…”

Section: Related Workmentioning

confidence: 99%

“…Besides this, we have also compared the results which are detected by our proposed method with those which are extracted by 6 state-of-the-art community-detection algorithms, namely, Fast Q [12], LPA [21], Walktrap [30], Attractor [32], IsoFdp [28], and WMW [14]; all of them have been introduced in Section 2. e comparison results will be presented and analyzed in Section 4.3.…”

Section: Network and Comparison Systemsmentioning

confidence: 99%

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A Seed-Expanding Method Based on TOPSIS for Community Detection in Complex Networks

et al. 2020

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The centrality plays an important role in many community-detection algorithms, which depend on various kinds of centralities to identify seed vertices of communities first and then expand each of communities based on the seeds to get the resulting community structure. The traditional algorithms always use a single centrality measure to recognize seed vertices from the network, but each centrality measure has both pros and cons when being used in this circumstance; hence seed vertices identified using a single centrality measure might not be the best ones. In this paper, we propose a framework which integrates advantages of various centrality measures to identify the seed vertices from the network based on the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) multiattribute decision-making technology. We take each of the centrality measures involved as an attribute, rank vertices according to the scores which are calculated for them using TOPSIS, and then take vertices with top ranks as the seeds. To put this framework into practice, we concretize it in this paper by considering four centrality measures as attributes to identify the seed vertices of communities first, then expanding communities by iteratively inserting one unclassified vertex into the community to which its most similar neighbor belongs, and the similarity between them is the largest among all pairs of vertices. After that, we obtain the initial community structure. However, the amount of communities might be much more than they should be, and some communities might be too small to make sense. Therefore, we finally consider a postprocessing procedure to merge some initial communities into larger ones to acquire the resulting community structure. To test the effectiveness of the proposed framework and method, we have performed extensive experiments on both some synthetic networks and some real-world networks; the experimental results show that the proposed method can get better results, and the quality of the detected community structure is much higher than those of competitors.

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Community detection in complex networks using density-based clustering algorithm and manifold learning

Cited by 43 publications

References 40 publications

Identifying multi-scale communities in networks by asymptotic surprise

Identifying multi-scale communities in networks by asymptotic surprise

A Markov Chain-Based Overlapping Community Detection Algorithm for Complex Networks

A Seed-Expanding Method Based on TOPSIS for Community Detection in Complex Networks

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