GNN papers in ICLR 2022

Why Propagate Alone? Parallel Use of Labels and Features on Graphs

• Decision: Accept (Poster) (8/6/5/5/5)

You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks

• Decision: Accept (Poster) (8/6/8/6)

Equivariant Heterogeneous Graph Networks

• Decision: Reject (5/5/5/5)

Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs

• Decision: Reject (5/8/6/5)

SpecTRA: Spectral Transformer for Graph Representation Learning

• Decision: Reject (5/5/3/5)

Dynamic Graph Representation Learning via Graph Transformer Networks

• Decision: Reject (5/5/6/3)

Coarformer: Transformer for large graph via graph coarsening

• Decision: Reject (3/6/5/8)

Automated Self-Supervised Learning for Graphs

• Decision: Accept (Poster) (6/8/5/6)

Query Embedding on Hyper-Relational Knowledge Graphs

• Decision: Accept (Poster) (6/6/5/5/8)

Graph Neural Networks with Learnable Structural and Positional Representations

• Decision: Accept (Poster) (6/6/5/5/8)

Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks

• Decision: Accept (Poster) (5/6/6/6)

GLASS: GNN with Labeling Tricks for Subgraph Representation Learning

• Decision: Accept (Poster) (6/6/6/5)

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