In recent years, significant advances have been made in the design and evaluation of
balanced (hyper) graph partitioning algorithms. We survey trends of the past decade in …

Recent Advances in Graph Partitioning | SpringerLink Skip to main content Advertisement
SpringerLink Account Menu Find a journal Publish with us Track your research Search Cart …

Large-scale graph-structured computation is central to tasks ranging from targeted
advertising to natural language processing and has led to the development of several graph …

Wilkinson defined a sparse matrix as one with enough zeros that it pays to take advantage of
them. 1 This informal yet practical definition captures the essence of the goal of direct …

Abstract The Open Porous Media (OPM) initiative is a community effort that encourages
open innovation and reproducible research for simulation of porous media processes. OPM …

Graph neural networks (GNNs) are among the most powerful tools in deep learning. They
routinely solve complex problems on unstructured networks, such as node classification …

Krylov subspace methods (KSMs) are iterative algorithms for solving large, sparse linear
systems and eigenvalue problems. Current KSMs rely on sparse matrix-vector multiply …

Hypergraphs are a generalization of graphs where edges (aka nets) are allowed to connect
more than two vertices. They have a similarly wide range of applications as graphs. This …

With the emergence of big graphs in a variety of real applications like social networks,
machine learning based on distributed graph-computing~(DGC) frameworks has attracted …

We describe a complete protocol for bit commitment based on the transmission of polarized
photons. We show that under the laws of quantum physics, this protocol cannot be cheated …