892 citations · 1.1k across the 17 of their papers we have counts for
3 papers · 1 filter
A Capsule Network-based Model for Learning Node Embeddings
Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen +1
In this paper, we focus on learning low-dimensional embeddings for nodes in graph-structured data. To achieve this, we propose Caps2NE -- a new unsupervised embedding model leverag…
Universal Graph Transformer Self-Attention Networks
Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung
We introduce a transformer-based GNN model, named UGformer, to learn graph representations. In particular, we present two UGformer variants, wherein the first variant (publicized i…
A Relational Memory-based Embedding Model for Triple Classification and Search Personalization
Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung
Knowledge graph embedding methods often suffer from a limitation of memorizing valid triples to predict new ones for triple classification and search personalization problems. To t…