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This is an official implementation of Multi-hop Attention Graph Neural Network (IJCAI2021).

Implementation Environment:

  • python >= 3.7
  • torch >= 1.4.0
  • numpy == 1.17.2
  • dgl-cu101 == 0.4.3

Tasks: Node classification and KG completion

  • Node classification

  •  datasets: Cora, Citeseer, Pubmed (from DGL dataset)

  •  codes --> node classification (train.py)

  • Knowledge Graph Completion

  •  datasets: Freebase237-15k and WordNet18RR (from https://github.com/villmow/datasets_knowledge_embedding)

  •  codes_kge --> KG emebdding (all the pakages with 'kge' are used for Knowledge graph embedding) (runkge.py)

  • parametertuning --> random search based hyper-parameter tuning

Hyper-parameter demonstration for MAGNA

  • in_feats: dimension of input node features (for KG embedding: in_ent_feats, in_rel_feats represent the dimension of
  • entity embedding and relation embedding, respectively)
  • hidden_dim: dimension of hidden dimension
  • num_heads: head number of multi-head attention,
  • alpha: transition probability in personal page-rank (0.05 ~ 0.25, data dependent)
  • hop_num: number of iterations to approximate (3 ~ 10, data dependent)
  • feat_drop: dropout ratio over the feature (input node features)
  • attn_drop: dropout ratio over attention matrix
  • topk_type='local': if the degree as nodes is large. ('local': select top-k for each head, otherwise select top-k
  • shared by all heads)
  • top_k=-1: top-k neighbor selection, -1 means no 'top-k' selection is performed

References

@inproceedings{wang2020multi, title={Multi-hop Attention Graph Neural Network}, author={Wang, Guangtao and Ying, Zhitao and Huang, Jing and Leskovec, Jure}, booktitle={International Joint Conference on Artificial Intelligence}, year={2021} }

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