2 citations · 3 across the 5 of their papers we have counts for
5 papers
SEG:Seeds-Enhanced Iterative Refinement Graph Neural Network for Entity Alignment
Wei Ai, Yinghui Gao, Jianbin Li +4
Entity alignment is crucial for merging knowledge across knowledge graphs, as it matches entities with identical semantics. The standard method matches these entities based on thei…
Graph Contrastive Learning via Cluster-refined Negative Sampling for Semi-supervised Text Classification
Wei Ai, Jianbin Li, Ze Wang +4
Graph contrastive learning (GCL) has been widely applied to text classification tasks due to its ability to generate self-supervised signals from unlabeled data, thus facilitating…
MCSFF: Multi-modal Consistency and Specificity Fusion Framework for Entity Alignment
Wei Ai, Wen Deng, Hongyi Chen +3
Multi-modal entity alignment (MMEA) is essential for enhancing knowledge graphs and improving information retrieval and question-answering systems. Existing methods often focus on…
Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations
Yuntao Shou, Wei Ai, Jiayi Du +3
The task of multi-modal emotion recognition in conversation (MERC) aims to analyze the genuine emotional state of each utterance based on the multi-modal information in the convers…
Fast Butterfly-Core Community Search For Large Labeled Graphs
JiaYi Du, Yinghao Wu, Wei Ai +3
Community Search (CS) aims to identify densely interconnected subgraphs corresponding to query vertices within a graph. However, existing heterogeneous graph-based community search…