Weighted Graph Clustering for Community Detection of Large Social Networks

Liu, Ruifang; Feng, Shuo; Shi, Ruisheng; Guo, Wenbin

doi:10.1016/j.procs.2014.05.248

Cited by 45 publications

(18 citation statements)

References 16 publications

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“…In this work, we used a community detection algorithm to identify the feature clusters. Detection of community structures in the weighted complex networks is significant to understand the network structures and analysis of the network properties [21,22]. Community detection is a very important problem in social network analysis.…”

Section: Proposed Methodsmentioning

confidence: 99%

Feature selection using social network techniques

Azadifar

Monadjemi

2015

2015 7th Conference on Information and Knowledge Technology (IKT)

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Feature selection is an important preprocessing step in machine learning and pattern recognition where in the former it is aimed at removing some irrelevant and/or redundant features from a given dataset. In this paper, a new graph theoretic based feature selection method is proposed. The proposed method uses the social network techniques to select the final feature set. In other word, the community detection algorithm with the node centrality measure are integrated for the feature selection problem. Furthermore, this method can be applied on both supervised and unsupervised modes. We also compared the performance of the proposed method with the well-known and state-of-the-art filter based feature selection methods. The results indicate that the efficiency and effectiveness of the proposed method as well as improvements over previous related methods can be seen.

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Section: Proposed Methodsmentioning

confidence: 99%

Feature selection using social network techniques

Azadifar

Monadjemi

2015

2015 7th Conference on Information and Knowledge Technology (IKT)

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“…Subgroup mining approaches search for important subgraphs within the text network based on the graph's structure or node attributes and identify topics by revealing various relationships between terms. Liu et al [37] proposed a weighted graph clustering algorithm with the purpose of community detection based on the concept of density and attractiveness. A user's core degree (Liu et al use social network data in their demonstration) is defined as node weight, with attractiveness then defined as edge weight.…”

Section: Related Workmentioning

confidence: 99%

Multi-Dimension Topic Mining Based on Hierarchical Semantic Graph Model

et al. 2020

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Topic mining of scientific literature can accurately capture the contextual structure of a topic, track research hotspots within a field, and improve the availability of information about the literature. This paper introduces a multi-dimensional topic mining method based on a hierarchical semantic graph model. The main innovations include (1) the hierarchical extraction of feature terms and construction of a corresponding semantic graph and (2) multi-dimensional topic mining based on graph segmentation and structure analysis. The process of semantic graph construction is based primarily on hierarchical feature term extraction, which can effectively reveal the hierarchical structural distribution of feature terms within documents. Our graph model also takes into account the complementarity of content-and context-related feature terms in documents while avoiding the loss of textual information. In addition, the multi-dimensional features of the topic can be mined effectively via an in-depth analysis of the constructed graph, resulting in a quantitative visualization of the many-to-many association between the topic and feature terms. A variety of experiments on existing document datasets demonstrate that the proposed approach is able to outperform state-of-the-art methods in terms of accuracy and efficacy. INDEX TERMS Topic mining, multi-dimensional topic, hierarchical semantic graph.

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“…The time complexity of their algorithm is O ((m + ∆ 2 ) n), where ∆ is an average vertex degree, n is the number of nodes and m is the total number of edges of the social network. Study in [39] proposed a method for detecting communities in large weighted social networks based on density and attractiveness to reduce the time complexity of the algorithms proposed in previous works. They chose this approach because a typical real-world dataset has weighted nodes and edges.…”

Section: B Dynamic Algorithmsmentioning

confidence: 99%

A Temporal User Attribute-Based Algorithm to Detect Communities in Online Social Networks

Mahmoudi¹,

Bakar

Sookhak

et al. 2020

IEEE Access

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The world is witnessing the daily emergence of a vast variety of online social networks and community detection problem is a major research area in online social network studies. The existing community detection algorithms are mostly edge-based and are evaluated using the modularity metric benchmarks. However, these algorithms have two inherent limitations. Firstly, they are based on a pure mathematical object which considers the number of connections in each community as the main measures. Consequently, a resolution limit and low accuracy in finding community members in often observed. Whereas, online social networks are dynamic networks and the key players are humans whose main attributes such as lifespan, geo-location, the density of interactions, and user weight, change over time. These attributes tend to influence the formation of user communities in any category of online social network. Secondly, the output structure of existing community detection algorithms is usually provided as a graph and dendrogram. A graph structure, is, however, characterized by a high memory complexity, and subsequently exponential search time complexity. Implementing dendrogram such a complex structure is complicated. To address memory complexity and the accuracy rate of the community detection issues, this paper proposes a new temporal user attribute-based algorithm, namely the recently largest interaction based on the attributes of a typical online social network user. Experimental results show that the proposed algorithm outperforms eight well-known algorithms in this domain.

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Weighted Graph Clustering for Community Detection of Large Social Networks

Cited by 45 publications

References 16 publications

Feature selection using social network techniques

Feature selection using social network techniques

Multi-Dimension Topic Mining Based on Hierarchical Semantic Graph Model

A Temporal User Attribute-Based Algorithm to Detect Communities in Online Social Networks

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