The world's appetite for analyzing massive amounts of structured and unstructured data has
grown dramatically. The computational demands of these abundant-data applications, such …

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 …

Background: Some developer activity traditionally performed manually, such as making
code commits, opening, managing, or closing issues is increasingly subject to automation in …

The value of graph-based big data can be unlocked by exploring the topology and metrics of
the networks they represent, and the computational approaches to this exploration take on …

As graphs continue to grow, external memory graph processing systems serve as a
promising alternative to inmemory solutions for low cost and high scalability. Unfortunately …

Communities are prevalent in social networks, knowledge graphs, and biological networks.
Recently, the topic of community search (CS), extracting a dense subgraph containing a …

Due to the limited capacity of GPU memory, the majority of prior work on graph applications
on GPUs has been restricted to graphs of modest sizes that fit in memory. Recent hardware …

We present KADABRA, a new algorithm to approximate betweenness centrality in directed
and undirected graphs, which significantly outperforms all previous approaches on real …

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 …

In this paper, we propose ExtraV, a framework for near-storage graph processing. It is based
on the novel concept of graph virtualization, which efficiently utilizes a cache-coherent …