Recent years have witnessed the emergence and flourishing of hierarchical graph pooling
neural networks (HGPNNs) which are effective graph representation learning approaches …

In recommender systems (RSs), predicting the next item that a user interacts with is critical
for user retention. While the last decade has seen an explosion of RSs aimed at identifying …

A recommender or recommendation system is a subclass of information filtering systems that
seeks to predict the “rating” or “preference” that a user would assign to an item. Although …

Graph representation learning (GRL) is a powerful technique for learning low-dimensional
vector representation of high-dimensional and often sparse graphs. Most studies explore the …

Graph embedding transforms high-dimensional graphs into a lower-dimensional vector
space while preserving their structural information and properties. Context-sensitive graph …

Graph representation learning, which maps high-dimensional graphs or sparse graphs into
a low-dimensional vector space, has shown its superiority in numerous learning tasks …

Graphs are a vastly useful and widely used form of modeling and representation of systems,
processes, entities, events, objects, components etc., in various domains of discourse, that …