Advances in Scalable and Memory-Efficient Community Detection

Community Detection and Network Analysis Trends

Recent advancements in the field of network analysis and community detection have seen significant innovations, particularly in handling complex and large-scale networks. The focus has shifted towards developing algorithms that are scalable, parameter-free, and capable of processing dynamic and attributed networks. These new methodologies aim to enhance the accuracy and efficiency of community detection, addressing the limitations of traditional spectral clustering methods, especially in signed and bipartite graphs.

One notable trend is the integration of semantic attributes and mesoscopic structures into community detection algorithms, which has led to more robust and contextually relevant community identification. Additionally, memory-efficient techniques using weighted sketches have been introduced to manage the computational challenges posed by large graphs, ensuring that these advanced algorithms can be applied in real-world scenarios without overwhelming system resources.

The field is also witnessing a growing interest in the analysis of hypergraphs and their spectral properties, with new formulations and clustering algorithms being developed to handle the complexities of group relations. Furthermore, the application of network analysis to specific domains, such as the film industry and anime communities, is providing valuable insights and driving the development of more tailored algorithms for community detection and recommendation systems.

Noteworthy Papers:

  • GraphC: Parameter-free Hierarchical Clustering of Signed Graph Networks v2 - Introduces a scalable algorithm for signed networks that outperforms existing methods by 18.64%.
  • HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community Detection - Proposes a novel model that integrates attribute semantics and mesoscopic structures, outperforming state-of-the-art methods.
  • Memory-Efficient Community Detection on Large Graphs Using Weighted Sketches - Presents memory-efficient alternatives for community detection algorithms, reducing memory demands while maintaining accuracy.

Sources

GraphC: Parameter-free Hierarchical Clustering of Signed Graph Networks v2

A Bellman-Ford algorithm for the path-length-weighted distance in graphs

Analyzing Social Networks of Actors in Movies and TV Shows

Effective Community Detection Over Streaming Bipartite Networks (Technical Report)

Centrality in Collaboration: A Novel Algorithm for Social Partitioning Gradients in Community Detection for Multiple Oncology Clinical Trial Enrollments

Clustering Based on Density Propagation and Subcluster Merging

HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community Detection

Memory-Efficient Community Detection on Large Graphs Using Weighted Sketches

Hypergraphs as Weighted Directed Self-Looped Graphs: Spectral Properties, Clustering, Cheeger Inequality

Analysis of Bipartite Networks in Anime Series: Textual Analysis, Topic Clustering, and Modeling

On the Complexity of 2-club Cluster Editing with Vertex Splitting

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