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 transformer model is known to be computationally demanding, and
proh...
Recent advances in large language models have raised wide concern in
gen...
Data with missing values is ubiquitous in many applications. Recent year...
The pandemic of COVID-19 has inspired extensive works across different
r...
Various tasks are reformulated as multi-label classification problems, i...
Graph representation learning has been widely studied and demonstrated
e...
In the research of end-to-end dialogue systems, using real-world knowled...
Pre-trained language models (PLMs) have made remarkable progress in
tabl...
Real-world data usually exhibits a long-tailed distribution,with a few
f...
Pre-trained language models (PLM) have achieved remarkable advancement i...
Most graph neural networks follow the message passing mechanism. However...
Recent works have revealed that Transformers are implicitly learning the...
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...
Relational structures such as schema linking and schema encoding have be...
Training deep learning (DL) models has become a norm. With the emergence...
Training Transformer-based models demands a large amount of data, while
...
Recently, improving the robustness of policies across different environm...
In recent years, multi-agent reinforcement learning (MARL) has presented...
A networked time series (NETS) is a family of time series on a given gra...
The rapid development of modern science and technology has spawned rich
...
Federated learning is emerging as a machine learning technique that trai...
The pursuit of knowledge is the permanent goal of human beings. Scientif...
The wide deployment of machine learning in recent years gives rise to a ...
The von Neumann graph entropy is a measure of graph complexity based on ...
Analyzing the groups in the network based on same attributes, functions ...
The robustness of deep neural networks against adversarial example attac...
Graph matching pairs corresponding nodes across two or more graphs. The
...
Influence maximization (IM) aims at maximizing the spread of influence b...
Author name ambiguity causes inadequacy and inconvenience in academic
in...
This paper investigates the problem of utilizing network topology and pa...
In this paper, we investigate the geometric structure of activation spac...
Community detection refers to the task of discovering groups of vertices...
Most existing knowledge graphs (KGs) in academic domains suffer from pro...
Most of today's high-speed switches and routers adopt an input-queued
cr...
Finding hot topics in scholarly fields can help researchers to keep up w...
A text network refers to a data type that each vertex is associated with...