Jul 6, 2020 · We present an efficient graph coarsening approach, based on Schur complements, for computing the embedding of the relevant vertices.
Abstract. Graph embeddings are a ubiquitous tool for ma- chine learning tasks, such as node classification and link prediction, on graph-structured data.
To address this, we present an efficient graph coarsening algorithm based on Schur complements that only computes the embeddings of the relevant vertices. We ...
Abstract. Graph embeddings are a ubiquitous tool for ma- chine learning tasks, such as node classification and link prediction, on graph-structured data.
We investigate how coarsening algorithms based on the Schur complement affect the predictive performance of random walk-based graph embeddings for different ...
matrices, we need to show that S is (i) symmetric, (ii) its off- diagonal entries are non-positive and (iii) for all i ∈ [n − 1] we have Sii ≥ − Pj6=i Sij.
Our experiments with a state-of-the-art, fast graph embedding tool show that there is an interplay between the coarsening decisions taken and the embedding ...
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My work brings together tools from many areas such as combinatorial data structures, algorithmic graph theory, numerical linear algebra, and metric embeddings.
They first summarize (some may use the term “coarsening”) the input graph into a smaller summary graph by grouping subsets of nodes into supernodes, and then ...
Oct 19, 2024 · Fahrbach et al. (2020) introduced a new coarsening algorithm based Schur complements to generate embeddings for the vertices in a large graph.