Temporal knowledge graphs, representing the dynamic relationships and
in...
Graph Neural Network (GNN) has demonstrated extraordinary performance in...
Large language models (LLMs)have achieved great success in general domai...
Researchers usually come up with new ideas only after thoroughly
compreh...
The pandemic of COVID-19 has inspired extensive works across different
r...
Graph representation learning has been widely studied and demonstrated
e...
In the research of end-to-end dialogue systems, using real-world knowled...
Temporal knowledge graph, serving as an effective way to store and model...
Understanding the origin and influence of the publication's idea is crit...
Graph-to-text (G2T) generation and text-to-graph (T2G) triple extraction...
User review data is helpful in alleviating the data sparsity problem in ...
With the tremendous expansion of graphs data, node classification shows ...
Spatio-temporal graph learning is a key method for urban computing tasks...
The rapid development of modern science and technology has spawned rich
...
The pursuit of knowledge is the permanent goal of human beings. Scientif...
The von Neumann graph entropy is a measure of graph complexity based on ...
Graph matching pairs corresponding nodes across two or more graphs. The
...
Influence maximization (IM) aims at maximizing the spread of influence b...
This paper investigates the problem of utilizing network topology and pa...
Finding hot topics in scholarly fields can help researchers to keep up w...