A Compressed Sensing Based Least Squares Approach to Semi-supervised Local Cluster Extraction
This work addresses the need for efficient local clustering in graph analysis, offering an incremental improvement over existing methods for researchers and practitioners in data mining and network analysis.
The paper tackles the problem of extracting local clusters from graphs by proposing a compressed sensing-based least squares semi-supervised algorithm, which finds desired clusters with high probability under weaker assumptions and with less computational complexity than prior work, as demonstrated through experiments on synthetic and real datasets like MNIST and political blogs.
A least squares semi-supervised local clustering algorithm based on the idea of compressed sensing is proposed to extract clusters from a graph with known adjacency matrix. The algorithm is based on a two-stage approach similar to the one in \cite{LaiMckenzie2020}. However, under a weaker assumption and with less computational complexity than the one in \cite{LaiMckenzie2020}, the algorithm is shown to be able to find a desired cluster with high probability. The ``one cluster at a time" feature of our method distinguishes it from other global clustering methods. Several numerical experiments are conducted on the synthetic data such as stochastic block model and real data such as MNIST, political blogs network, AT\&T and YaleB human faces data sets to demonstrate the effectiveness and efficiency of our algorithm.