In recent years, data and computing resources are typically distributed in the devices of end
users, various regions or organizations. Because of laws or regulations, the distributed data …

Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

Graph neural networks (GNNs) have shown their superiority in modeling graph data. Owing
to the advantages of federated learning, federated graph learning (FGL) enables clients to …

Federated learning has emerged as an important paradigm for training machine learning
models in different domains. For graph-level tasks such as graph classification, graphs can …

Graph neural networks (GNNs) have attracted extensive research attention in recent years
due to their capability to progress with graph data and have been widely used in practical …

Knowledge graph embedding plays an important role in knowledge representation,
reasoning, and data mining applications. However, for multiple cross-domain knowledge …

Graph machine learning has gained great attention in both academia and industry recently.
Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are …

Federated Learning (FL) recently emerges as a paradigm to train a global machine learning
model across distributed clients without sharing raw data. Knowledge Graph (KG) …

Knowledge graphs (KGs) consisting of a large number of triples have become widespread
recently, and many knowledge graph embedding (KGE) methods are proposed to embed …

Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-
world tasks on graph data, consisting of node features and the adjacent information between …