Recent demands for storing and querying big data have revealed various shortcomings of
traditional relational database systems. This, in turn, has led to the emergence of a new kind …

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

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 …

Graphs are one of the key data structures for many real-world computing applications and
the importance of graph analytics is ever-growing. While existing software graph processing …

X-Stream is a system for processing both in-memory and out-of-core graphs on a single
shared-memory machine. While retaining the scatter-gather programming model with state …

Subgraph matching finds all distinct isomorphic embeddings of a query graph on a data
graph. For large graphs, current solutions face the scalability challenge due to expensive …

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 …

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 …

Chaos scales graph processing from secondary storage to multiple machines in a cluster.
Earlier systems that process graphs from secondary storage are restricted to a single …

In the big data era, much real-world data can be naturally represented as graphs.
Consequently, many application domains can be modeled as graph processing. Graph …