Graph convolutional networks (GCNs) have a strong ability to learn graph representation
and have achieved good performance in a range of applications, including social …

Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …

Graph Convolutional Networks (GCNs) have recently been shown to be quite successful in
modeling graph-structured data. However, the primary focus has been on handling simple …

In many real-world network datasets such as co-authorship, co-citation, email
communication, etc., relationships are complex and go beyond pairwise. Hypergraphs …

Compared to sequential learning models, graph-based neural networks exhibit some
excellent properties, such as ability capturing global information. In this paper, we …

Abstract Knowledge graphs are multi-relational data that contain massive entities and
relations. As an effective graph representation technique based on deep learning, graph …

Hierarchical text classification is an essential yet challenging subtask of multi-label text
classification with a taxonomic hierarchy. Existing methods have difficulties in modeling the …

Most existing methods capture semantic information by using attention mechanism or joint
learning, ignoring inter-clause dependency. However, inter-clause dependency contains …

Word representation plays a key role in natural language processing (NLP). Various
representation methods have been developed, among which pre-trained word embeddings …

Dealing with relational data always required significant computational resources, domain
expertise and task-dependent feature engineering to incorporate structural information into a …