Graph machine learning models have been successfully deployed in a variety of application areas. One of the most prominent types of models - Graph Neural Networks (GNNs) - provides an elegant way of extracting expressive node-level representation …
Learning representations of nodes has been a crucial area of the graph machine learning research area. A well-defined node embedding model should reflect both node features and the graph structure in the final embedding. In the case of dynamic …
Representation learning for graphs has attracted increasing attention in recent years. In this paper, we define and study a new problem of learning attributed graph embeddings. Our setting considers how to update existing node representations from …
In recent years, dynamic graph embedding has attracted a lot of attention due to its usefulness in real-world scenarios. In this paper, we consider discrete-time dynamic graph representation learning, where embeddings are computed for each time …