Advancing Local Clustering on Graphs via Compressive Sensing: Semi-supervised and Unsupervised Methods
arXiv:2504.19419v3 Announce Type: replace Abstract: Local clustering aims to identify specific substructures within a large graph without any additional structural information of the graph. These substructures are typically small compared to the overall graph, enabling the problem to be approached by finding a sparse solution to a linear system associated with the graph Laplacian. In this work, we first propose a method for identifying specific local clusters when very few labeled data are given, which we term semi-supervised local clustering. We then extend this approach to the unsupervised s
The paper was recently published, reflecting ongoing research advancements in graph-based machine learning methods, driven by the increasing complexity and scale of real-world data networks.
Improved local clustering methods, especially unsupervised ones, can enhance the efficiency and accuracy of analyzing large, complex datasets, having implications across various AI applications.
This work introduces new techniques for local clustering that require less labeled data, potentially reducing the training overhead and increasing the applicability of such methods to diverse problems.
- · AI/ML researchers
- · Big data analytics platforms
- · Security intelligence tools
- · Social network analysis
- · Traditional graph clustering methods
- · Data analysis techniques requiring extensive labeling
More efficient and accurate identification of substructures within vast datasets becomes possible.
This could lead to breakthroughs in areas like anomaly detection, community finding, and targeted intervention strategies in large networks.
The reduced need for labeled data could democratize advanced graph analysis, allowing smaller entities to leverage sophisticated AI techniques.
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Read at arXiv cs.LG