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<1> Introduction

code of HetGNN in KDD2019 paper: Heterogeneous Graph Neural Network

Contact: Chuxu Zhang ([email protected])

<2> How to use

python HetGNN.py [parameters]

(enable GPU: python HetGNN.py --cuda 1)

#test academic data: (author) A_n - 28646, (paper) P_n - 21044, ((venue) V_n - 18

<3> Data requirement (academic data)

a_p_list_train.txt: paper neighbor list of each author in training data

p_a_list_train.txt: author neighbor list of each paper in training data

p_p_citation_list.txt: paper citation neighbor list of each paper

v_p_list_train.txt: paper neighbor list of each venue in training data

p_v.txt: venue of each paper

p_title_embed.txt: pre-trained paper title embedding

p_abstract_embed.txt: pre-trained paper abstract embedding

node_net_embedding.txt: pre-trained node embedding by network embedding

het_neigh_train.txt: generated neighbor set of each node by random walk with re-start

het_random_walk.txt: generated random walks as node sequences (corpus) for model training

<4> Model evaluation and raw data processing code will be uploaded later

<5> If you find code useful, please consider citing our work.

Heterogeneous Graph Neural Network

Zhang, Chuxu and Song, Dongjin and Huang, Chao and Swami, Ananthram and Chawla, Nitesh V.

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD '19