In this work we propose a random graph model that can produce graphs at different levels of sparsity. We analyze how sparsity affects the graph spectra, and thus the performance of graph neural ...

In this paper, we present a novel method of incorporating dense (e.g., depth, RGB-D) data in a general purpose least-squares graph optimization framework. Rather than employing a loosely coupled, ...

We introduce and develop a theory of limits for sequences of sparse graphs based on Lp graphons, which generalizes both the existing L1 theory of dense graph limits and its extension by Bollob\u0013as ...

We extend the Lp theory of sparse graph limits, which was introduced in a companion paper, by analyzing different notions of convergence. Under suitable restrictions on node weights, we prove the ...

Researchers from the University of Massachusetts Amherst and Google DeepMind have introduced a novel sparse-matrix factorization-based method. This method optimally computes latent query and item ...

DRL models for graph partitioning and sparse matrix ordering. - alga ... agent to refine the partitions obtained by interpolating back the partition on a coarser representation of the graph. ... units ...

Official implementation of the paper "Understanding Sparse Neural Networks from their Topology via Multipartite Graph Representations", TMLR, April 2024. Authors: Elia Cunegatti, Matteo Farina, Doina ...

These sparse trees are a reduced representation of the full binary tree, where gene flow patterns with identical outcomes are combined into symmetric and premature leaf nodes (Fig. 1c).