Comparative Analysis of Community Detection Algorithms on the SNAP Social Circles Dataset

• URL: http://arxiv.org/abs/2502.04341v1
• Date: Sat, 01 Feb 2025 23:38:09 GMT
• Title: Comparative Analysis of Community Detection Algorithms on the SNAP Social Circles Dataset
• Authors: Yash Malode, Amit Aylani, Arvind Bhardwaj, Deepak Hajoary,
• Abstract summary: We conduct a comparative analysis of several prominent community detection algorithms applied to the SNAP Social Circles dataset. We evaluate the performance of these algorithms based on various metrics such as modularity, normalized cut-ratio, silhouette score, compactness, and separability.
• Score: 0.0
• License:
• Abstract: In network research, Community Detection has always been a topic of significant interest in network science, with numerous papers and algorithms proposing to uncover the underlying structures within networks. In this paper, we conduct a comparative analysis of several prominent community detection algorithms applied to the SNAP Social Circles Dataset, derived from the Facebook Social Media network. The algorithms implemented include Louvain, Girvan-Newman, Spectral Clustering, K-Means Clustering, etc. We evaluate the performance of these algorithms based on various metrics such as modularity, normalized cut-ratio, silhouette score, compactness, and separability. Our findings reveal insights into the effectiveness of each algorithm in detecting various meaningful communities within the social network, shedding light on their strength and limitations. This research contributes to the understanding of community detection methods and provides valuable guidance for their application in analyzing real-world social networks.

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)
• Implicit models, latent compression, intrinsic biases, and cheap lunches in community detection [0.0]
  Community detection aims to partition a network into clusters of nodes to summarize its large-scale structure. Some community detection methods are inferential, explicitly deriving the clustering objective through a probabilistic generative model. Other methods are descriptive, dividing a network according to an objective motivated by a particular application. We present a solution that associates any community detection objective, inferential or descriptive, with its corresponding implicit network generative model.
  arXiv  Detail & Related papers  (2022-10-17T15:38:41Z)
• 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 multilevel clustering technique for community detection [0.0]
  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.
  arXiv  Detail & Related papers  (2021-01-16T23:26:44Z)
• 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)
• Next Waves in Veridical Network Embedding [4.544287346584366]
  We propose a framework for network embedding algorithms and discuss how the principles of predictability, computability and stability apply. The utilization of this framework in network embedding holds the potential to motivate and point to new directions for future research.
  arXiv  Detail & Related papers  (2020-07-10T13:41:48Z)
• 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.