Graph self-supervised learning (SSL), including contrastive and generative approaches,
offers great potential to address the fundamental challenge of label scarcity in real-world …

Local graph clustering methods aim to find a cluster of nodes by exploring a small region of
the graph. These methods are attractive because they enable targeted clustering around a …

The heat kernel is a type of graph diffusion that, like the much-used personalized PageRank
diffusion, is useful in identifying a community nearby a starting seed node. We present the …

In this work we study the widely used\emphspectral clustering algorithms, ie partition a
graph into k clusters via (1) embedding the vertices of a graph into a low-dimensional space …

This work studies the classical spectral clustering algorithm which embeds the vertices of
some graph G=(V_G, E_G) into R^ k using k eigenvectors of some matrix of G, and applies k …

We study the problem of computing a minimum cut in a simple, undirected graph and give a
deterministic O(m\log^2n\log\log^2n) time algorithm. This improves on both the best …

Given a subset A of vertices of an undirected graph G, the cut-improvement problem asks us
to find a subset S that is similar to A but has smaller conductance. An elegant algorithm for …

Given a large graph, how can we calculate the relevance between nodes fast and
accurately? Random walk with restart (RWR) provides a good measure for this purpose and …

Graph clustering has many important applications in computing, but due to growing sizes of
graphs, even traditionally fast clustering methods such as spectral partitioning can be …

Hypergraph-based machine learning methods are now widely recognized as important for
modeling and using higher-order and multiway relationships between data objects. Local …