Heterogeneous Hypergraph Embedding for Graph Classification

Thumbnail Image
File version

Accepted Manuscript (AM)

Sun, Xiangguo
Yin, Hongzhi
Liu, Bo
Chen, Hongxu
Cao, Jiuxin
Shao, Yingxia
Viet Hung, Nguyen Quoc
Griffith University Author(s)
Primary Supervisor
Other Supervisors
File type(s)



Recently, graph neural networks have been widely used for network embedding because of their prominent performance in pairwise relationship learning. In the real world, a more natural and common situation is the coexistence of pairwise relationships and complex non-pairwise relationships, which is, however, rarely studied. In light of this, we propose a graph neural network-based representation learning framework for heterogeneous hypergraphs, an extension of conventional graphs, which can well characterize multiple non-pairwise relations. Our framework first projects the heterogeneous hypergraph into a series of snapshots and then we take the Wavelet basis to perform localized hypergraph convolution. Since the Wavelet basis is usually much sparser than the Fourier basis, we develop an efficient polynomial approximation to the basis to replace the time-consuming Laplacian decomposition. Extensive evaluations have been conducted and the experimental results show the superiority of our method. In addition to the standard tasks of network embedding evaluation such as node classification, we also apply our method to the task of spammers detection and the superior performance of our framework shows that relationships beyond pairwise are also advantageous in the spammer detection. To make our experiment repeatable, source codes and related datasets are available at https://xiangguosun.mystrikingly.com

Journal Title
Conference Title

Proceedings of the 14th ACM International Conference on Web Search and Data Mining

Book Title
Thesis Type
Degree Program
Publisher link
Patent number
Grant identifier(s)
Rights Statement
Rights Statement

© ACM, 2021. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, ISBN: 978-1-4503-8297-7, https://doi.org/10.1145/3437963.3441835

Item Access Status
Access the data
Related item(s)

Artificial intelligence


Persistent link to this record

Sun, X; Yin, H; Liu, B; Chen, H; Cao, J; Shao, Y; Viet Hung, NQ, Heterogeneous Hypergraph Embedding for Graph Classification, Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 2021