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Found 5 papers, 4 papers with code
Spectral Toolkit of Algorithms for Graphs: Technical Report (1)
Spectral Toolkit of Algorithms for Graphs (STAG) is an open-source library for efficient spectral graph algorithms, and its development starts in September 2022.
On Learning the Structure of Clusters in Graphs
Graph clustering is a fundamental problem in unsupervised learning, with numerous applications in computer science and in analysing real-world data.
A Tighter Analysis of Spectral Clustering, and Beyond
For the second result, we show that, by applying fewer than $k$ eigenvectors to construct the embedding, spectral clustering is able to produce better output for many practical instances; this result is the first of its kind in spectral clustering.
Finding Bipartite Components in Hypergraphs
Hypergraphs are important objects to model ternary or higher-order relations of objects, and have a number of applications in analysing many complex datasets occurring in practice.
Local Algorithms for Finding Densely Connected Clusters
Local graph clustering is an important algorithmic technique for analysing massive graphs, and has been widely applied in many research fields of data science.
