New Recipe for Semi-supervised Community Detection: Clique Annealing under Crystallization Kinetics

• URL: http://arxiv.org/abs/2504.15927v1
• Date: Tue, 22 Apr 2025 14:17:15 GMT
• Title: New Recipe for Semi-supervised Community Detection: Clique Annealing under Crystallization Kinetics
• Authors: Ling Cheng, Jiashu Pu, Ruicheng Liang, Qian Shao, Hezhe Qiao, Feida Zhu,
• Abstract summary: We propose CLique ANNealing (CLANN), which integrates kinetics concepts to community detection.<n>In particular, we liken community detection to identifying a crystal subgrain (core) that expands into a complete grain (community) through a process similar to annealing.<n>In experiments on textbf43 different network settings, CLANN outperforms state-of-the-art methods across multiple real-world datasets.
• Score: 4.808367525484134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semi-supervised community detection methods are widely used for identifying specific communities due to the label scarcity. Existing semi-supervised community detection methods typically involve two learning stages learning in both initial identification and subsequent adjustment, which often starts from an unreasonable community core candidate. Moreover, these methods encounter scalability issues because they depend on reinforcement learning and generative adversarial networks, leading to higher computational costs and restricting the selection of candidates. To address these limitations, we draw a parallel between crystallization kinetics and community detection to integrate the spontaneity of the annealing process into community detection. Specifically, we liken community detection to identifying a crystal subgrain (core) that expands into a complete grain (community) through a process similar to annealing. Based on this finding, we propose CLique ANNealing (CLANN), which applies kinetics concepts to community detection by integrating these principles into the optimization process to strengthen the consistency of the community core. Subsequently, a learning-free Transitive Annealer was employed to refine the first-stage candidates by merging neighboring cliques and repositioning the community core, enabling a spontaneous growth process that enhances scalability. Extensive experiments on \textbf{43} different network settings demonstrate that CLANN outperforms state-of-the-art methods across multiple real-world datasets, showcasing its exceptional efficacy and efficiency in community detection.

Related papers

• Community Detection by ELPMeans: An Unsupervised Approach That Uses Laplacian Centrality and Clustering [0.0]
  Community in network analysis has become more intricate due to the recent hike in social networks.<n>This paper suggests a new approach named ELPMeans that strives to address this challenge.
  arXiv  Detail & Related papers  (2025-02-27T09:07:45Z)
• GCC: Generative Calibration Clustering [55.44944397168619]
  We propose a novel Generative Clustering (GCC) method to incorporate feature learning and augmentation into clustering procedure. First, we develop a discrimirative feature alignment mechanism to discover intrinsic relationship across real and generated samples. Second, we design a self-supervised metric learning to generate more reliable cluster assignment.
  arXiv  Detail & Related papers  (2024-04-14T01:51:11Z)
• A Unified and General Framework for Continual Learning [58.72671755989431]
  Continual Learning (CL) focuses on learning from dynamic and changing data distributions while retaining previously acquired knowledge. Various methods have been developed to address the challenge of catastrophic forgetting, including regularization-based, Bayesian-based, and memory-replay-based techniques. This research aims to bridge this gap by introducing a comprehensive and overarching framework that encompasses and reconciles these existing methodologies.
  arXiv  Detail & Related papers  (2024-03-20T02:21:44Z)
• VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates [67.51637355249986]
  We study an extension of the Block Model (SBM), a widely used statistical framework for community detection. We propose a novel algorithm based on iterative refinement techniques and show that it optimally recovers the latent communities. We rigorously assess the added value of leveraging edge's side information in the community detection process.
  arXiv  Detail & Related papers  (2024-02-29T02:19:55Z)
• Safe Multi-agent Learning via Trapping Regions [89.24858306636816]
  We apply the concept of trapping regions, known from qualitative theory of dynamical systems, to create safety sets in the joint strategy space for decentralized learning. We propose a binary partitioning algorithm for verification that candidate sets form trapping regions in systems with known learning dynamics, and a sampling algorithm for scenarios where learning dynamics are not known.
  arXiv  Detail & Related papers  (2023-02-27T14:47:52Z)
• 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)
• Uncovering the Local Hidden Community Structure in Social Networks [20.467702194064525]
  We propose a new method that detects and boosts each layer iteratively on a subgraph sampled from the original network. We theoretically show that our method can avoid some situations that a broken community and the local community are regarded as one community in the subgraph.
  arXiv  Detail & Related papers  (2021-12-08T04:07:19Z)
• Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id [109.1730454118532]
  Unsupervised person re-identification (Re-Id) has attracted increasing attention due to its practical application in the read-world video surveillance system. We present the hybrid dynamic cluster contrast and probability distillation algorithm. It formulates the unsupervised Re-Id problem into an unified local-to-global dynamic contrastive learning and self-supervised probability distillation framework.
  arXiv  Detail & Related papers  (2021-09-29T02:56:45Z)
• 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)
• Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing [81.71105567425275]
  We develop the first certified robustness guarantee of community detection against adversarial structural perturbation. We theoretically show that the smoothed community detection method provably groups a given arbitrary set of nodes into the same community. We also empirically evaluate our method on multiple real-world graphs with ground truth communities.
  arXiv  Detail & Related papers  (2020-02-09T18:39:39Z)

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.