Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

The vertex-centric programming model is an established computational paradigm recently
incorporated into distributed processing frameworks to address challenges in large-scale …

Natural graphs with skewed distributions raise unique challenges to distributed graph
computation and partitioning. Existing graph-parallel systems usually use a “one-size-fits-all” …

Graph-structured analytics has been widely adopted in a number of big data applications
such as social computation, web-search and recommendation systems. Though much prior …

Processing a one trillion-edge graph has recently been demonstrated by distributed graph
engines running on clusters of tens to hundreds of nodes. In this paper, we employ a single …

In this paper, several works have been presented related to clustering parallel computing for
distributed systems. The trend of the paper is to focus on the strengths of previous works in …

Because of the importance of graph workloads and the limitations of CPUs/GPUs, many
graph processing accelerators have been proposed. The basic approach of prior …

Graph is a well known data structure to represent the associated relationships in a variety of
applications, eg, data science and machine learning. Despite a wealth of existing efforts on …

This article presents GRAPE, a parallel GRAP h E ngine for graph computations. GRAPE
differs from prior systems in its ability to parallelize existing sequential graph algorithms as a …

The world is becoming a more conjunct place and the number of data sources such as
social networks, online transactions, web search engines, and mobile devices is increasing …