There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

Converting student theses or final projects into publishable articles poses a significant
challenge. Many students struggle to articulate their ideas in scientific publications due to a …

Knowledge graphs (KGs) have received increasing attention due to its wide applications on
natural language processing. However, its use case on temporal question answering (QA) …

Many models that leverage knowledge graphs (KGs) have recently demonstrated
remarkable success in question answering (QA) tasks. In the real world, many facts …

Attributed networks, as a manifestation of data in non-Euclidean domains, have a wide
range of applications in the real world, such as molecular property prediction, social network …

Node classification is an essential problem in graph learning. However, many models
typically obtain unsatisfactory performance when applied to few-shot scenarios. Some …

Short text classification, as a research subtopic in natural language processing, is more
challenging due to its semantic sparsity and insufficient labeled samples in practical …

Many models that leverage knowledge graphs (KGs) have recently demonstrated
remarkable success in question answering (QA) tasks. In the real world, many facts …

Text Classification (TC) is a fundamental task in the information retrieval community.
Nowadays, the mainstay TC methods are built on the deep neural networks, which can learn …