Graph-based Semi-supervised Local Clustering with Few Labeled Nodes
This work addresses local clustering for graph analysis, but it is incremental as it builds on existing frameworks with a specific enhancement.
The paper tackles the problem of local clustering in graphs with few labeled nodes by proposing a semi-supervised approach that improves initial cut quality, resulting in effective performance across various datasets.
Local clustering aims at extracting a local structure inside a graph without the necessity of knowing the entire graph structure. As the local structure is usually small in size compared to the entire graph, one can think of it as a compressive sensing problem where the indices of target cluster can be thought as a sparse solution to a linear system. In this paper, we apply this idea based on two pioneering works under the same framework and propose a new semi-supervised local clustering approach using only few labeled nodes. Our approach improves the existing works by making the initial cut to be the entire graph and hence overcomes a major limitation of the existing works, which is the low quality of initial cut. Extensive experimental results on various datasets demonstrate the effectiveness of our approach.