arXiv:2412.03008v2 Announce Type: replace-cross Abstract: Local/seeded clustering aims to find a compact cluster near the given starting instances. While most existing studies on graph clustering assume a discrete graph setting (i.e., unweighted, undirected graphs without self-loops), real-world graphs can be more complex. In this paper, we extend the classic non-approximating Andersen-Chung-Lang (ACL) clustering algorithm beyond discrete graphs and generalize its quadratic optimality to a wider range of complex graphs, including weighted, directed, and self-looped graphs and hypergraphs with
This paper extends fundamental algorithms for graph clustering, a core component in many AI and data science applications, indicating a continued focus on improving foundational AI capabilities.
Improved clustering algorithms for complex graphs can lead to more accurate and efficient analysis of real-world data, impacting diverse fields from social networks to biological systems.
The ability to apply the Andersen-Chung-Lang algorithm to a wider range of graph types, including weighted, directed, and hypergraphs, enhances the robustness and applicability of local clustering methods.
- · AI researchers
- · Data scientists
- · Big data analytics companies
- · Social network analysis platforms
More accurate and scalable local clustering will become possible across complex real-world datasets.
This foundational improvement could enable new AI applications or significantly enhance existing ones where understanding local data structure is critical.
As AI models leverage these improved clustering methods, the efficiency and insight derived from very large, complex datasets could increase, accelerating discovery in various scientific and commercial domains.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG