Community detection has become a fundamental operation in numerous graph-theoretic
applications. It is used to reveal natural divisions that exist within real world networks without …

We propose a novel hashing scheme for image retrieval, clustering and automatic object
discovery. Unlike commonly used bag-of-words approaches, the spatial extent of image …

This paper introduces the largest-log-degree-first (LLF) and smallest-log-degree-last (SLL)
ordering heuristics for parallel greedy graph-coloring algorithms, which are inspired by the …

Abstract The Graph Coloring Problem (GCP) can be simply defined as partitioning the
vertices of a graph into independent sets while minimizing the number of colors used. So far …

We show that recent innovations in deep reinforcement learning can effectively color very
large graphs--a well-known NP-hard problem with clear commercial applications. Because …

We present a suite of fast and effective algorithms, encapsulated in a software package
called ColPack, for a variety of graph coloring and related problems. Many of the coloring …

We develop the first parallel graph coloring heuristics with strong theoretical guarantees on
work and depth and coloring quality. The key idea is to design a relaxation of the vertex …

Graph algorithms are challenging to parallelize on manycore architectures due to complex
data dependencies and irregular memory access. We consider the well studied problem of …

Graph algorithms are notorious for not getting good speedup on parallel architectures.
These algorithms tend to suffer from irregular dependencies and a high synchronization cost …

Influence maximization is an advanced graph-theoretic operation that aims to identify a set
of k most influential nodes in a network. The problem is of immense interest in many network …