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NeurIPS 2019: HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs
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hypergcn.py

README.md

HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs

Conference Paper

Source code for NeurIPS 2019 paper: HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs

Overview of HyperGCN: *Given a hypergraph and node features, HyperGCN approximates the hypergraph by a graph in which each hyperedge is approximated by a subgraph consisting of an edge between maximally disparate nodes and edges between each of these and every other node (mediator) of the hyperedge. A graph convolutional network (GCN) is then run on the resulting graph approximation. *

Dependencies

  • Compatible with PyTorch 1.0 and Python 3.x.

Dataset:

  • DBLP dataset used in the paper is included in the data directory.
  • Other datasets (Cora, Citeseer, Pubmed) used in the paper can be downloaded from here

Training model (Node classifiction):

  • To start training run:

    python hypergcn.py -mediators True -split 0
    • -mediators denotes whether to use mediators (True) or not (False)
    • -split is the train-test split number

Citation:

@incollection{hypergcn_neurips19,
title = {HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs},
author = {Yadati, Naganand and Nimishakavi, Madhav and Yadav, Prateek and Nitin, Vikram and Louis, Anand and Talukdar, Partha},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS) 32},
pages = {1509--1520},
year = {2019},
publisher = {Curran Associates, Inc.}
}
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