The non-Euclidean geometry of hyperbolic spaces has recently garnered
co...
Maximizing the user-item engagement based on vectorized embeddings is a
...
Hyperbolic space is emerging as a promising learning space for represent...
Graph-structured data are widespread in real-world applications, such as...
Considering the prevalence of the power-law distribution in user-item
ne...
Graphs are a popular data type found in many domains. Numerous technique...
In large-scale recommender systems, the user-item networks are generally...
Given the ubiquitous existence of graph-structured data, learning the
re...
Link prediction is a key problem for network-structured data, attracting...
Graph neural networks generalize conventional neural networks to
graph-s...
Recently, hyperbolic space has risen as a promising alternative for
semi...
Aiming to alleviate data sparsity and cold-start problems of traditional...
Representation learning over temporal networks has drawn considerable
at...
Dynamic graphs arise in a plethora of practical scenarios such as social...