The history of artificial intelligence (AI) has witnessed the significant impact of high-quality
data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently …

The history of artificial intelligence (AI) has witnessed the significant impact of high-quality
data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently …

While numerous effective decentralized algorithms have been proposed with theoretical
guarantees and empirical successes, the performance limits in decentralized optimization …

Given a graph G, an edge-coloring is an assignment of colors to edges of G such that any
two edges sharing an endpoint receive different colors. By Vizing's celebrated theorem, any …

Given a graph $ G $, an edge-coloring is an assignment of colors to edges of $ G $ such that
any two edges sharing an endpoint receive different colors. By Vizing's celebrated theorem …

Hierarchical clustering over graphs is a fundamental task in data mining and machine
learning with applications in many domains including phylogenetics, social network …

Training on large-scale graphs has achieved remarkable results in graph representation
learning, but its cost and storage have raised growing concerns. As one of the most …

A (1±ϵ)-sparsifier of a hypergraph G(V,E) is a (weighted) subgraph that preserves the value
of every cut to within a (1±ϵ)-factor. It is known that every hypergraph with n vertices admits …

The seminal work of Ahn, Guha, and McGregor in 2012 introduced the graph sketching
technique and used it to present the first streaming algorithms for various graph problems …

In this paper we study the problem of finding $(\epsilon,\phi) $-expander decompositions of
a graph in the streaming model, in particular for dynamic streams of edge insertions and …