4 citations · 7 across the 4 of their papers we have counts for
4 papers
Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications
Han Xie, Da Zheng, Jun Ma +9
Model pre-training on large text corpora has been demonstrated effective for various downstream applications in the NLP domain. In the graph mining domain, a similar analogy can be…
Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs
Costas Mavromatis, Vassilis N. Ioannidis, Shen Wang +6
How can we learn effective node representations on textual graphs? Graph Neural Networks (GNNs) that use Language Models (LMs) to encode textual information of graphs achieve state…
Win-Win Cooperation: Bundling Sequence and Span Models for Named Entity Recognition
Bin Ji, Shasha Li, Jie Yu +2
For Named Entity Recognition (NER), sequence labeling-based and span-based paradigms are quite different. Previous research has demonstrated that the two paradigms have clear compl…
Knowledge-aware Neural Collective Matrix Factorization for Cross-domain Recommendation
Li Zhang, Yan Ge, Jun Ma +2
Cross-domain recommendation (CDR) can help customers find more satisfying items in different domains. Existing CDR models mainly use common users or mapping functions as bridges be…