A multilevel clustering technique for community detection

• URL: http://arxiv.org/abs/2101.06551v1
• Date: Sat, 16 Jan 2021 23:26:44 GMT
• Title: A multilevel clustering technique for community detection
• Authors: Isa Inuwa-Dutse, Mark Liptrott, Yannis Korkontzelos
• Abstract summary: This study presents a novel detection method based on a scalable framework to identify related communities in a network. We propose a multilevel clustering technique (MCT) that leverages structural and textual information to identify local communities termed microcosms. The approach offers a better understanding and clarity toward describing how low-level communities evolve and behave on Twitter.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A network is a composition of many communities, i.e., sets of nodes and edges with stronger relationships, with distinct and overlapping properties. Community detection is crucial for various reasons, such as serving as a functional unit of a network that captures local interactions among nodes. Communities come in various forms and types, ranging from biologically to technology-induced ones. As technology-induced communities, social media networks such as Twitter and Facebook connect a myriad of diverse users, leading to a highly connected and dynamic ecosystem. Although many algorithms have been proposed for detecting socially cohesive communities on Twitter, mining and related tasks remain challenging. This study presents a novel detection method based on a scalable framework to identify related communities in a network. We propose a multilevel clustering technique (MCT) that leverages structural and textual information to identify local communities termed microcosms. Experimental evaluation on benchmark models and datasets demonstrate the efficacy of the approach. This study contributes a new dimension for the detection of cohesive communities in social networks. The approach offers a better understanding and clarity toward describing how low-level communities evolve and behave on Twitter. From an application point of view, identifying such communities can better inform recommendation, among other benefits.

Related papers

• Enhancing Community Detection in Networks: A Comparative Analysis of Local Metrics and Hierarchical Algorithms [49.1574468325115]
  This study employs the same method to evaluate the relevance of using local similarity metrics for community detection. The efficacy of these metrics was evaluated by applying the base algorithm to several real networks with varying community sizes.
  arXiv  Detail & Related papers  (2024-08-17T02:17:09Z)
• Community Detection for Heterogeneous Multiple Social Networks [6.863667633281842]
  The community plays a crucial role in understanding user behavior and network characteristics in social networks. This paper presents a community detection method based on nonnegative matrix tri-factorization for multiple social networks. The effectiveness of the proposed method is evaluated with new metrics on Twitter, Instagram, and Tumblr.
  arXiv  Detail & Related papers  (2024-05-07T14:52:34Z)
• MGTCOM: Community Detection in Multimodal Graphs [0.34376560669160383]
  MGTCOM is an end-to-end framework optimizing network embeddings, communities and the number of communities in tandem. Our method is competitive against state-of-the-art and performs well in inductive inference.
  arXiv  Detail & Related papers  (2022-11-10T16:11:03Z)
• Co-Membership-based Generic Anomalous Communities Detection [0.0]
  We introduce the Co-Membership-based Generic Anomalous Communities Detection Algorithm (referred as to CMMAC) CMMAC is domain-free and almost unaffected by communities' sizes and densities. We present an algorithm for generating a community-structured random network enabling the infusion of anomalous communities.
  arXiv  Detail & Related papers  (2022-03-30T12:23:55Z)
• A Comprehensive Survey on Community Detection with Deep Learning [93.40332347374712]
  A community reveals the features and connections of its members that are different from those in other communities in a network. This survey devises and proposes a new taxonomy covering different categories of the state-of-the-art methods. The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders.
  arXiv  Detail & Related papers  (2021-05-26T14:37:07Z)
• A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning [95.27249880156256]
  We develop and present a unified architecture of network community-finding methods. We introduce a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical model and deep learning. We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.
  arXiv  Detail & Related papers  (2021-01-03T02:32:45Z)
• On the use of local structural properties for improving the efficiency of hierarchical community detection methods [77.34726150561087]
  We study how local structural network properties can be used as proxies to improve the efficiency of hierarchical community detection. We also check the performance impact of network prunings as an ancillary tactic to make hierarchical community detection more efficient.
  arXiv  Detail & Related papers  (2020-09-15T00:16:12Z)
• Community detection and Social Network analysis based on the Italian wars of the 15th century [0.0]
  We study social network modelling by using human interaction as a basis. We propose a new set of functions, affinities, designed to capture the nature of the local interactions among each pair of actors in a network. We develop a new community detection algorithm, the Borgia Clustering, where communities naturally arise from the multi-agent interaction in the network.
  arXiv  Detail & Related papers  (2020-07-06T11:05:07Z)
• Deep Learning for Community Detection: Progress, Challenges and Opportunities [79.26787486888549]
  Article summarizes the contributions of the various frameworks, models, and algorithms in deep neural networks. This article summarizes the contributions of the various frameworks, models, and algorithms in deep neural networks.
  arXiv  Detail & Related papers  (2020-05-17T11:22:11Z)
• Detecting Communities in Heterogeneous Multi-Relational Networks:A Message Passing based Approach [89.19237792558687]
  Community is a common characteristic of networks including social networks, biological networks, computer and information networks. We propose an efficient message passing based algorithm to simultaneously detect communities for all homogeneous networks.
  arXiv  Detail & Related papers  (2020-04-06T17:36:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.