Fast consensus clustering in complex networks

Tandon, Aditya; Albeshri, Aiiad; Thayananthan, Vijey; Alhalabi, Wadee; Fortunato, Santo

doi:10.1103/physreve.99.042301

Cited by 31 publications

(23 citation statements)

References 18 publications

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“…This method is based on the consensus matrix, which calculates some of the nodes instead of computing the total nodes, and therefore runtime reduces. 16 Another algorithm is designed based on link clustering. Since links represent unique connections among nodes, the link clustering will detect groups of links with the same features.…”

Section: Clustering Methodsmentioning

confidence: 99%

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Bakhthemmat

Izadi

2021

Big Data

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Community detection in social networks is one of the advertising methods in electronic marketing. One of the approaches to find communities in large social networks is to use greedy methods, because these methods perform very fast. Greedy methods are generally designed based on local decisions; thus, inappropriate local decisions may result in an improper global solution. The use of a greedy improved index with a futuristic approach can, to some extent, prevent inappropriate local choices. Our proposed method determines the influential nodes in the social network based on the followers and following and new futuristic greedy index. It classifies the nodes based on the influential nodes by the density-based clustering algorithm with a new distance function. The proposed method can improve clustering precision to detect communities by the futuristic greedy approach. We implemented the proposed algorithm with the map-reduce technique in the Hadoop structure. Experimental results in datasets show that the average of the rand index of clusters was accomplished by 99.32% in the proposed method. In addition, these results illustrate that there is a reduction in execution time by the proposed algorithm.

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Section: Clustering Methodsmentioning

confidence: 99%

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Bakhthemmat

Izadi

2021

Big Data

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“…[23], [50]- [54]). Tandon et al [45] proposed a fast consensus clustering method for networks, which includes a sampling scheme in its computation. Burgess et al [46] proposed a consensus clustering method using link prediction.…”

Section: B Existing Methods For Consensus Clusteringmentioning

confidence: 99%

Consensus Clustering of Tweet Networks via Semantic and Sentiment Similarity Estimation

et al. 2019

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Although Twitter has become an important source of information, the number of accessible tweets is too large for users to easily find their desired information. To overcome this difficulty, a method for tweet clustering is proposed in this paper. Inspired by the reports that network representation is useful for multimedia content analysis including clustering, a network-based approach is employed. Specifically, a consensus clustering method for tweet networks that represent relationships among the tweets' semantics and sentiment are newly derived. The proposed method integrates multiple clustering results obtained by applying successful clustering methods to the tweet networks. By integrating complementary clustering results obtained based on semantic and sentiment features, the accurate clustering of tweets becomes feasible. The contribution of this work can be found in the utilization of the features, which differs from existing network-based consensus clustering methods that target only the network structure. Experimental results for a real-world Twitter dataset, which includes 65 553 tweets of 25 datasets, verify the effectiveness of the proposed method.

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“…by the Louvain method [21]) and semantic (e.g. by k-means [91]) information are further fused via consensus (ensemble-based) clustering techniques [82,118,187,189,190]. 7.1.…”

Section: Late Fusion Methodsmentioning

confidence: 99%

Community Detection in Attributed Social Networks: A Unified Weight-Based Model and Its Regimes

Chunaev

Nuzhdenko

Bochenina

2019

2019 International Conference on Data Mining Workshops (ICDMW)

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Community detection is a fundamental problem in social network analysis consisting, roughly speaking, in dividing social actors (modelled as nodes in a social graph) with certain social connections (modelled as edges in the social graph) into densely knitted and highly related groups with each group well separated from the others. Classical approaches for community detection usually deal only with the structure of the network and ignore features of the nodes, although major real-world networks provide additional actors' information such as age, gender, interests, etc., traditionally called node attributes. It is known that the attributes may clarify and enrich the knowledge about the actors and give sense to the detected communities. This has led to a relatively novel direction in community detection -constructing algorithms that use both the structure and the attributes of the network (modelled already via a nodeattributed graph) to yield more informative and qualitative results.During the last decade many methods based on different ideas and techniques have appeared in this direction. Although there exist some partial overviews of them, a recent survey is a necessity as the growing number of the methods may cause uncertainty in practice.In this paper we aim at clarifying the overall situation by proposing a clear classification of the methods and providing a comprehensive survey of the available results. We not only group and analyse the corresponding methods but also focus on practical aspects, including the information which methods outperform others and which datasets and quality measures are used for evaluation.

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Fast consensus clustering in complex networks

Cited by 31 publications

References 18 publications

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Consensus Clustering of Tweet Networks via Semantic and Sentiment Similarity Estimation

Community Detection in Attributed Social Networks: A Unified Weight-Based Model and Its Regimes

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