Paper Info
Title
HGCN: A Heterogeneous Graph Convolutional Network-Based Deep Learning Model Toward Collective Classification
Abstract
Collective classification, as an important technique to study networked data, aims to exploit the label autocorrelation for a group of inter-connected entities with complex dependencies. As the emergence of various heterogeneous information networks (HINs), collective classification at present is confronting several severe challenges stemming from the heterogeneity of HINs, such as complex relational hierarchy, potential incompatible semantics and node-context relational semantics. To address the challenges, in this paper, we propose a novel heterogeneous graph convolutional network-based deep learning model, called HGCN, to collectively categorize the entities in HINs. Our work involves three primary contributions: i) HGCN not only learns the latent relations from the relation-sophisticated HINs via multi-layer heterogeneous convolutions, but also captures the semantic incompatibility among relations with properly-learned edge-level filter parameters; ii) to preserve the fine-grained relational semantics of different-type nodes, we propose a heterogeneous graph convolution to directly tackle the original HINs without any in advance transforming the network from heterogeneity to homogeneity; iii) we perform extensive experiments using four real-world datasets to validate our proposed HGCN, the multi-facet results show that our proposed HGCN can significantly improve the performance of collective classification compared with the state-of-the-art baseline methods.
Year
DOI
Venue
2020
10.1145/3394486.3403169
KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event CA USA July, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-7998-4
1
PageRank 
References 
Authors
0.34
20
4
Name
Order
Citations
PageRank
Zhihua Zhu1304.99
Xinxin Fan2165.10
Xiaokai Chu362.15
Jingping Bi47018.36