In recent years, significant advances have been made in the design and evaluation of
balanced (hyper) graph partitioning algorithms. We survey trends of the past decade in …

Graph neural network (GNN) has been widely applied in real-world applications, such as
product recommendation in e-commerce platforms and risk control in financial management …

Graph processing workloads are memory intensive with irregular access patterns and large
memory footprint resulting in low data locality. Their popular software implementations …

GNN's training needs to resolve issues of vertex dependencies, ie, each vertex
representation's update depends on its neighbors. Existing distributed GNN systems adopt …

GraphScope is a system and a set of language extensions that enable a new programming
interface for large-scale distributed graph computing. It generalizes previous graph …

Graph neural networks (GNNs) aim to learn a low-dimensional feature for each vertex in the
graph from its input high-dimensional feature, by aggregating the features of the vertex's …

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 …

Graph edge partitioning is an important prepro-cessing step to optimize distributed
computing jobs on graph-structured data. The edge set of a given graph is split into k equally …

This paper studies incremental graph partitioning. Given a (vertex-cut or edge-cut) partition
C (G) of a graph G and updates ΔG to G, it is to compute changes ΔO to C (G), yielding a …

Network science methodology is increasingly applied to a large variety of real-world
phenomena, often leading to big network data sets. Thus, networks (or graphs) with millions …