44 citations · 109 across the 8 of their papers we have counts for
8 papers
Compressing Deep Graph Neural Networks via Adversarial Knowledge Distillation
Huarui He, Jie Wang, Zhanqiu Zhang +1
Deep graph neural networks (GNNs) have been shown to be expressive for modeling graph-structured data. Nevertheless, the over-stacked architecture of deep graph models makes it dif…
Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings
Jie Wang, Zhanqiu Zhang, Zhihao Shi +3
Semantic matching models -- which assume that entities with similar semantics have similar embeddings -- have shown great power in knowledge graph embeddings (KGE). Many existing s…
Rethinking Graph Convolutional Networks in Knowledge Graph Completion
Zhanqiu Zhang, Jie Wang, Jieping Ye +1
Graph convolutional networks (GCNs) -- which are effective in modeling graph structures -- have been increasingly popular in knowledge graph completion (KGC). GCN-based KGC models…
ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs
Zhanqiu Zhang, Jie Wang, Jiajun Chen +2
Query embedding (QE) -- which aims to embed entities and first-order logical (FOL) queries in low-dimensional spaces -- has shown great power in multi-hop reasoning over knowledge…
Technical Report of Team GraphMIRAcles in the WikiKG90M-LSC Track of OGB-LSC @ KDD Cup 2021
Jianyu Cai, Jiajun Chen, Taoxing Pan +2
Link prediction in large-scale knowledge graphs has gained increasing attention recently. The OGB-LSC team presented OGB Large-Scale Challenge (OGB-LSC), a collection of three real…
Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion
Zhanqiu Zhang, Jianyu Cai, Jie Wang
Tensor factorization based models have shown great power in knowledge graph completion (KGC). However, their performance usually suffers from the overfitting problem seriously. Thi…
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
Zhanqiu Zhang, Jianyu Cai, Yongdong Zhang +1
Knowledge graph embedding, which aims to represent entities and relations as low dimensional vectors (or matrices, tensors, etc.), has been shown to be a powerful technique for pre…
Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets
Jie Wang, Jieping Ye
Sparse-Group Lasso (SGL) has been shown to be a powerful regression technique for simultaneously discovering group and within-group sparse patterns by using a combination of the $\…