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 …

Federated learning (FL) involves training a model over massive distributed devices, while
kee** the training data localized and private. This form of collaborative learning exposes …

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 …

This article presents GRAPE, a parallel GRAP h E ngine for graph computations. GRAPE
differs from prior systems in its ability to parallelize existing sequential graph algorithms as a …

Graph partitioning is crucial to parallel computations on large graphs. The choice of
partitioning strategies has strong impact on not only the performance of graph algorithms …

In general, the performance of parallel graph processing is determined by three pairs of
critical parameters, namely synchronous or asynchronous execution mode (Sync or Async) …

Federated learning (FL) is typically performed in a synchronous parallel manner, and the
involvement of a slow client delays the training progress. Current FL systems employ a …

This article proposes a notion of parametric simulation to link entities across a relational
database 𝒟 and a graph G. Taking functions and thresholds for measuring vertex closeness …

This paper proposes a class of graph association rules, denoted by GARs, to specify
regularities between entities in graphs. A GAR is a combination of a graph pattern and a …

Parameter server (PS) as the state-of-the-art distributed framework for large-scale iterative
machine learning tasks has been extensively studied. However, existing PS-based systems …