266 citations · 413 across the 9 of their papers we have counts for
17 papers
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020
Zhen Xu, Lanning Wei, Huan Zhao +4
Graph structured data is ubiquitous in daily life and scientific areas and has attracted increasing attention. Graph Neural Networks (GNNs) have been proved to be effective in mode…
Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones
Yushi Bai, Rex Ying, Hongyu Ren +1
Hierarchical relations are prevalent and indispensable for organizing human knowledge captured by a knowledge graph (KG). The key property of hierarchical relations is that they in…
Neural Distance Embeddings for Biological Sequences
Gabriele Corso, Rex Ying, Michal Pándy +3
The development of data-dependent heuristics and representations for biological sequences that reflect their evolutionary distance is critical for large-scale biological research.…
Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification
Xiaochen Hou, Peng Qi, Guangtao Wang +4
Recent work on aspect-level sentiment classification has demonstrated the efficacy of incorporating syntactic structures such as dependency trees with graph neural networks(GNN), b…
Identity-aware Graph Neural Networks
Jiaxuan You, Jonathan Gomes-Selman, Rex Ying +1
Message passing Graph Neural Networks (GNNs) provide a powerful modeling framework for relational data. However, the expressive power of existing GNNs is upper-bounded by the 1-Wei…
Design Space for Graph Neural Networks
Jiaxuan You, Rex Ying, Jure Leskovec
The rapid evolution of Graph Neural Networks (GNNs) has led to a growing number of new architectures as well as novel applications. However, current research focuses on proposing a…