Z
Zhitao Ying
Researcher at Stanford University
Publications - 6
Citations - 3519
Zhitao Ying is an academic researcher from Stanford University. The author has contributed to research in topics: Graph (abstract data type) & Matching (graph theory). The author has an hindex of 6, co-authored 6 publications receiving 3454 citations.
Papers
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Proceedings Article
Inductive Representation Learning on Large Graphs
TL;DR: GraphSAGE as mentioned in this paper is a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings instead of training individual embedding for each node.
Proceedings Article
Hierarchical graph representation learning with differentiable pooling
TL;DR: DiffPool as discussed by the authors learns a differentiable soft cluster assignment for nodes at each layer of a deep GNN, mapping nodes to a set of clusters, which then form the coarsened input for the next GNN layer.
Proceedings Article
GNNExplainer: Generating Explanations for Graph Neural Networks
TL;DR: GNNExplainer as mentioned in this paper identifies a compact subgraph structure and a small subset of node features that have a crucial role in GNN's prediction, and generates consistent and concise explanations for an entire class of instances.
Journal Article
Neural Subgraph Matching
TL;DR: NeuroMatch is an accurate, efficient, and robust neural approach to subgraph matching that decomposes query and target graphs into small subgraphs and embeds them using graph neural networks.
Proceedings Article
Design Space for Graph Neural Networks
TL;DR: GraphGym as discussed by the authors is a platform for exploring different GNN designs and tasks, including a general GNN design space and a GNN task space with a similarity metric, so that for a given novel task/dataset, we can quickly identify/transfer the best performing architecture; and an efficient and effective design space evaluation method which allows insights to be distilled from a huge number of model-task combinations.