Towards Direct Comparison of Community Structures in Social Networks

• URL: http://arxiv.org/abs/2209.12841v1
• Date: Mon, 26 Sep 2022 16:46:31 GMT
• Title: Towards Direct Comparison of Community Structures in Social Networks
• Authors: Soumita Das, Anupam Biswas
• Abstract summary: Community detection algorithms are evaluated by comparing evaluation metric values for the communities obtained with different algorithms. The evaluation metrics that are used for measuring quality of the communities incorporate the topological information of entities like connectivity of the nodes within or outside the communities. A quality measure namely emphTopological Variance (TV) is designed based on direct comparison of topological information of the communities.
• Score: 2.1320960069210475
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Community detection algorithms are in general evaluated by comparing evaluation metric values for the communities obtained with different algorithms. The evaluation metrics that are used for measuring quality of the communities incorporate the topological information of entities like connectivity of the nodes within or outside the communities. However, while comparing the metric values it loses direct involvement of topological information of the communities in the comparison process. In this paper, a direct comparison approach is proposed where topological information of the communities obtained with two algorithms are compared directly. A quality measure namely \emph{Topological Variance (TV)} is designed based on direct comparison of topological information of the communities. Considering the newly designed quality measure, two ranking schemes are developed. The efficacy of proposed quality metric as well as the ranking scheme is studied with eight widely used real-world datasets and six community detection algorithms.

Related papers

• Cost-Effective Community-Hierarchy-Based Mutual Voting Approach for Influence Maximization in Complex Networks [54.366995393644586]
  Real-world usually have high requirements on the balance between time and accuracy of influential nodes identification. This article proposes a novel approach called Cost-Effective Community-Hierarchy-Based Mutual Voting for influence in complex networks. The proposed approach outperforms 16 state-of-the-art techniques on the balance between time complexity and accuracy of influential nodes identification.
  arXiv  Detail & Related papers  (2024-09-21T06:32:28Z)
• 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)
• Normalized mutual information is a biased measure for classification and community detection [0.4779196219827508]
  We argue that results returned by the normalized mutual information are biased for two reasons. We show that one's conclusions about which algorithm is best are significantly affected by the biases in the traditional mutual information.
  arXiv  Detail & Related papers  (2023-07-03T18:12:32Z)
• Semi-supervised Community Detection via Structural Similarity Metrics [0.0]
  We study a semi-supervised community detection problem in which the objective is to estimate the community label of a new node. We propose an algorithm that computes a structural similarity metric' between the new node and each of the $K$ communities. Our findings highlight, to the best of our knowledge, the first semi-supervised community detection algorithm that offers theoretical guarantees.
  arXiv  Detail & Related papers  (2023-06-01T19:02:50Z)
• Uncertainty in GNN Learning Evaluations: The Importance of a Consistent Benchmark for Community Detection [4.358468367889626]
  We propose a framework to establish a common evaluation protocol for Graph Neural Networks (GNNs) We motivate and justify it by demonstrating the differences with and without the protocol. We find that by ensuring the same evaluation criteria is followed, there may be significant differences from the reported performance of methods at this task.
  arXiv  Detail & Related papers  (2023-05-10T10:22:28Z)
• Enhance Ambiguous Community Structure via Multi-strategy Community Related Link Prediction Method with Evolutionary Process [7.239725647907488]
  We design a new community attribute based link prediction strategy HAP. This paper aims at providing a community enhancement measure through adding links to clarify ambiguous community structures. The experimental results on twelve real-world datasets with ground truth community indicate that the proposed link prediction method outperforms other baseline methods.
  arXiv  Detail & Related papers  (2022-04-28T06:24:16Z)
• Riemannian classification of EEG signals with missing values [67.90148548467762]
  This paper proposes two strategies to handle missing data for the classification of electroencephalograms. The first approach estimates the covariance from imputed data with the $k$-nearest neighbors algorithm; the second relies on the observed data by leveraging the observed-data likelihood within an expectation-maximization algorithm. As results show, the proposed strategies perform better than the classification based on observed data and allow to keep a high accuracy even when the missing data ratio increases.
  arXiv  Detail & Related papers  (2021-10-19T14:24:50Z)
• 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)
• 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)
• Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark [57.10850350508929]
  We aim to provide a unified framework to summarize and evaluate existing research on heterogeneous network embedding (HNE) As the first contribution, we provide a generic paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms. As the second contribution, we create four benchmark datasets with various properties regarding scale, structure, attribute/label availability, and etcfrom different sources. As the third contribution, we create friendly interfaces for 13 popular HNE algorithms, and provide all-around comparisons among them over multiple tasks and experimental settings.
  arXiv  Detail & Related papers  (2020-04-01T03:42:11Z)

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.