Fast Heuristic Algorithm for Multi-scale Hierarchical Community Detection

Castrillo, Eduar; León, Elizabeth; Gómez, Jonatan

doi:10.1145/3110025.3110125

Cited by 12 publications

(16 citation statements)

References 26 publications

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“…e former works in a divisive way, which iteratively removes the edge with the largest betweenness from the network, until all the edges are removed; the latter operates in an agglomerative approach, which takes every vertex as a singlemember community first and then repeatedly merge two communities until all the vertices are assigned to the same community. WMW [14] defines a new dynamic structural similarity index and applies it to a heuristic agglomerative hierarchical algorithm which not only merges clusters with maximal similarity, but also merges clusters that do not meet the parameterized community definition to extract communities. In addition to this, some algorithms try to integrate the divisive way and agglomerative approach to detect communities.…”

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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Section: Related Workmentioning

confidence: 99%

Section: Network and Comparison Systemsmentioning

confidence: 99%

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

et al. 2020

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“…And Kmeans method subsequently calculates communities from the generated embeddings 4 . • BigClam [38]: A non-negative matrix factorization method to detect overlapping communities in large scale graphs 4 . • WMW [4]: A fast heuristic method for hierarchical community detection inspired by agglomerative hierarchical clustering 5 .…”

Section: Baselines and Settingsmentioning

confidence: 99%

“…https://github.com/melifluos/LSH-community-detection4 https://github.com/snap-stanford/snap/ 5 https://github.com/eduarc/WMW…”

mentioning

confidence: 99%

Detecting User Community in Sparse Domain via Cross-Graph Pairwise Learning

Gao

Jiang

et al. 2020

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

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Cyberspace hosts abundant interactions between users and different kinds of objects, and their relations are often encapsulated as bipartite graphs. Detecting user community in such heterogeneous graphs is an essential task to uncover user information needs and to further enhance recommendation performance. While several main cyber domains carrying high-quality graphs, unfortunately, most others can be quite sparse. However, as users may appear in multiple domains (graphs), their high-quality activities in the main domains can supply community detection in the sparse ones, e.g., user behaviors on Google can help thousands of applications to locate his/her local community when s/he uses Google ID to login those applications. In this paper, our model, Pairwise Cross-graph Community Detection (PCCD), is proposed to cope with the sparse graph problem by involving external graph knowledge to learn user pairwise community closeness instead of detecting direct communities. Particularly in our model, to avoid taking excessive propagated information, a two-level filtering module is utilized to select the most informative connections through both community and node level filters. Subsequently, a Community Recurrent Unit (CRU) is designed to estimate pairwise user community closeness. Extensive experiments on two real-world graph datasets validate our model against several strong alternatives. Supplementary experiments also validate its robustness on graphs with varied sparsity scales. CCS CONCEPTS • Information systems → Information retrieval; • Unsupervised learning → Cluster analysis; • Machine learning approaches → Neural networks.

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“…The structural similarity measures mentioned above, and other similars have been effectively used in graph clustering tasks [5], [8]- [11]. However, those similarities present a main drawback, i.e., those are limited to the immediate neighborhood of the connected vertices being measured.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Structural Similarity on Graphs

Castrillo,

León,

Gómez

2018

Preprint

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One way of characterizing the topological and structural properties of vertices and edges in a graph is by using structural similarity measures. Measures like Cosine, Jaccard and Dice compute the similarities restricted to the immediate neighborhood of the vertices, bypassing important structural properties beyond the locality. Others measures, such as the generalized edge clustering coefficient, go beyond the locality but with high computational complexity, making them impractical in large-scale scenarios. In this paper we propose a novel similarity measure that determines the structural similarity by dynamically diffusing and capturing information beyond the locality. This new similarity is modeled as an iterated function that can be solved by fixed point iteration in super-linear time and memory complexity, so it is able to analyze large-scale graphs. In order to show the advantages of the proposed similarity in the community detection task, we replace the local structural similarity used in the SCAN algorithm with the proposed similarity measure, improving the quality of the detected community structure and also reducing the sensitivity to the parameter of the SCAN algorithm.

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Fast Heuristic Algorithm for Multi-scale Hierarchical Community Detection

Cited by 12 publications

References 26 publications

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

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

Detecting User Community in Sparse Domain via Cross-Graph Pairwise Learning

Dynamic Structural Similarity on Graphs

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