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 …

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 …

A graph neural network (GNN) enables deep learning on structured graph data. There are
two major GNN training obstacles: 1) it relies on high-end servers with many GPUs which …

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” …

The performance bottlenecks of graph applications depend not only on the algorithm and
the underlying hardware, but also on the size and structure of the input graph. As a result …

There has been significant recent interest in parallel graph processing due to the need to
quickly analyze the large graphs available today. Many graph codes have been designed …

There is a growing need to perform a diverse set of real-time analytics (batch and stream
analytics) on evolving graphs to deliver the values of big data to users. The key requirement …

This paper introduces a new approach to building distributed-memory graph analytics
systems that exploits heterogeneity in processor types (CPU and GPU), partitioning policies …

Graph mining is an important category of graph algorithms that aim to discover structural
patterns such as cliques and motifs in a graph. While a great deal of work has been done …

We propose a new framework for computing the embeddings of large-scale graphs on a
single machine. A graph embedding is a fixed length vector representation for each node …