Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

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 …

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” …

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 networks (GNNs) have shown excellent performance in a wide range of
applications such as recommendation, risk control, and drug discovery. With the increase in …

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

The success of online social networks has attracted a constant interest in attacking and
exploiting them. Attackers usually control malicious accounts, including both fake and …

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 …

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