Embedding static graphs in low-dimensional vector spaces plays a key role in network
analytics and inference, supporting applications like node classification, link prediction, and …

Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

Network embedding techniques are powerful to capture structural regularities in networks
and to identify similarities between their local fabrics. However, conventional network …

Graphs have become a crucial way to represent large, complex and often temporal datasets
across a wide range of scientific disciplines. However, when graphs are used as input to …

With the success of Graph Neural Networks (GNNs) on non-Euclidean data, some GNN-
based approaches to network representation learning have emerged in recent years. Graph …

Link prediction can be used to extract missing information, identify spurious interactions as
well as forecast network evolution. Network embedding is a methodology to assign …

In the past few years, various methods have been developed that attempt to embed graph
nodes (eg users that interact through a social platform) onto low-dimensional vector spaces …

Learning on temporal graphs has attracted tremendous research interest due to its wide
range of applications. Some works intuitively merge graph neural networks (GNNs) and …

Graph representation learning (GRL) has become a new learning paradigm, supporting a
wide range of tasks such as node classification, link prediction, and graph classification …