We reduce the cost of communication and synchronization in graph processing by analyzing
the fastest way to process graphs: pushing the updates to a shared state or pulling the …

Existing distributed graph analytics systems are categorized into two main groups: those that
focus on efficiency with a risk of out-of-memory error and those that focus on scale-up with a …

Parameter server (PS) as the state-of-the-art distributed framework for large-scale iterative
machine learning tasks has been extensively studied. However, existing PS-based systems …

Designing distributed graph systems has drawn a lot of research interests due to the strong
expressiveness of the graph model and rapidly increasing graph volume. Most of them …

Many iterative graph processing systems have recently been developed to analyze graphs.
Although they are effective from different aspects, there is an important issue that has not …

Many graph algorithms are iterative in nature and can be supported by distributed memory-
based systems in a synchronous manner. However, an asynchronous model has been …

The high training costs of graph neural networks (GNNs) have limited their applicability on
large graphs, eg, graphs with hundreds of millions of vertices which have become common …

In recent years, a number of out-of-core graph processing systems have been proposed to
process graphs with billions of edges on just one commodity computer, due to their high cost …

Data partitioning is the most fundamental procedure before parallelizing complex analysis
on very big graphs. As a classical NP-complete problem, graph partitioning usually employs …

In this paper, we study parallel query processing with a focus on reducing the
communication cost, which is the dominating factor in parallel query processing. The …