Dynamic Community Detection

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

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Most implemented papers archive 2025-07-28

5 shown of 5 papers with code (11 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• A Spectral Framework for Tracking Communities in Evolving Networks
  10 Dec 2024 2 repositories listed
  Discovering and tracking communities in time-varying networks is an important task in network science, motivated by applications in fields ranging from neuroscience to sociology.
• Learning Persistent Community Structures in Dynamic Networks via Topological Data Analysis
  6 Jan 2024 1 repository listed Syntology ran 5 of 8 samples · 3 unverified · 8 pointer-only (licence)
  Dynamic community detection methods often lack effective mechanisms to ensure temporal consistency, hindering the analysis of network evolution.
• Transfer Learning Based Multi-Objective Genetic Algorithm for Dynamic Community Detection
  30 Sep 2021 1 repository listed
  In this paper, we propose a Feature Transfer Based Multi-Objective Optimization Genetic Algorithm (TMOGA) based on transfer learning and traditional multi-objective evolutionary algorithm framework.
• Phylogenetic reconstruction of the cultural evolution of electronic music via dynamic community detection (1975-1999)
  4 Nov 2020 1 repository listed
  The method used in this study is available as a new R package called DynCommPhylo.
• IncNSA: Detecting communities incrementally from time-evolving networks based on node similarity
  16 Jul 2020 1 repository listed
  When the network evolves from the previous snapshot to the current one, the proposed method only considers the community affiliations of partial nodes efficiently, which are either newborn nodes or some active nodes…

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.