With the widespread use of information technologies, information networks are becoming
increasingly popular to capture complex relationships across various disciplines, such as …

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 …

Embedding a web-scale information network into a low-dimensional vector space facilitates
tasks such as link prediction, classification, and visualization. Past research has addressed …

The interdependence between nodes in graphs is key to improve class prediction on nodes,
utilized in approaches like Label Probagation (LP) or in Graph Neural Networks (GNNs) …

Embedding models for deterministic Knowledge Graphs (KG) have been extensively
studied, with the purpose of capturing latent semantic relations between entities and …

Graph Machine Learning (Graph ML) has witnessed substantial advancements in recent
years. With their remarkable ability to process graph-structured data, Graph ML techniques …

We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to
scenarios where no input graph is given and increases their robustness to adversarial …

Networks are a general language for describing complex systems of interacting entities. In
the real world, a network always contains massive nodes, edges and additional complex …

With the prevalence of Internet access and online services, various big graphs are
generated in many real applications (eg, online social networks and knowledge graphs). An …

Graph Convolutional Network (GCN) has exhibited strong empirical performance in many
real-world applications. The vast majority of existing works on GCN primarily focus on the …