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 …

With the rapid development of information technologies, various big graphs are prevalent in
many real applications (eg, social media and knowledge bases). An important component of …

Simple graph algorithms such as PageRank have been the target of numerous hardware
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …

Graph processing is becoming increasingly prevalent across many application domains. In
spite of this prevalence, there is little research about how graphs are actually used in …

Numerous irregular graph datasets, for example social networks or web graphs, may contain
even trillions of edges. Often, their structure changes over time and they have domain …

Graph processing is becoming increasingly prevalent across many application domains. In
spite of this prevalence, there is little research about how graphs are actually used in …

High-performance implementations of graph algorithms are challenging to implement on
new parallel hardware such as GPUs because of three challenges:(1) the difficulty of coming …

Temporal property graphs are graphs whose structure and properties change over time.
Temporal graph datasets tend to be large due to stored historical information, asking for …

Owing to the lack of a universal definition of communities, some semi-supervised community
detection approaches learn the concept of community structures from known communities …

Efficient processing of large-scale graphs in distributed environments has been an
increasingly popular topic of research in recent years. Inter-connected data that can be …