3 citations · 6 across the 13 of their papers we have counts for
5 papers · 1 filter
Exploring Sparsity in Graph Transformers
Chuang Liu, Yibing Zhan, Xueqi Ma +5
Graph Transformers (GTs) have achieved impressive results on various graph-related tasks. However, the huge computational cost of GTs hinders their deployment and application, espe…
Careful Selection and Thoughtful Discarding: Graph Explicit Pooling Utilizing Discarded Nodes
Chuang Liu, Wenhang Yu, Kuang Gao +5
Graph pooling has been increasingly recognized as crucial for Graph Neural Networks (GNNs) to facilitate hierarchical graph representation learning. Existing graph pooling methods…
On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling
Chuang Liu, Yibing Zhan, Baosheng Yu +4
A pooling operation is essential for effective graph-level representation learning, where the node drop pooling has become one mainstream graph pooling technology. However, current…
Prompt-Learning for Cross-Lingual Relation Extraction
Chiaming Hsu, Changtong Zan, Liang Ding +5
Relation Extraction (RE) is a crucial task in Information Extraction, which entails predicting relationships between entities within a given sentence. However, extending pre-traine…
State of the Art and Potentialities of Graph-level Learning
Zhenyu Yang, Ge Zhang, Jia Wu +9
Graphs have a superior ability to represent relational data, like chemical compounds, proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as inp…