An increasing number of machine learning tasks require dealing with large graph datasets,
which capture rich and complex relationship among potentially billions of elements. Graph …

Big data is typically characterized with 4V's: Volume, Velocity, Variety and Veracity. When it
comes to big graphs, these challenges become even more staggering. Each and every of …

Graph neural networks (GNNs) have emerged due to their success at modeling graph data.
Yet, it is challenging for GNNs to efficiently scale to large graphs. Thus, distributed GNNs …

Besides its NP-completeness, the strict constraints of subgraph isomorphism are making it
impractical for graph pattern matching (GPM) in the context of big data. As a result, relaxed …

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 …

There is growing interest in graph pattern mining (GPM) problems such as motif counting.
GPM systems have been developed to provide unified interfaces for programming …

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 …

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 …

This special issue and our editorial celebrate 10 years of progress with data-intensive or
scientific workflows. There have been very substantial advances in the representation of …

Graph mining algorithms that aim at identifying structural patterns of graphs are typically
more complex than graph computation algorithms such as breadth first search. Researchers …