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dc.contributor.authorSun, Xiangguo
dc.contributor.authorYin, Hongzhi
dc.contributor.authorLiu, Bo
dc.contributor.authorChen, Hongxu
dc.contributor.authorCao, Jiuxin
dc.contributor.authorShao, Yingxia
dc.contributor.authorViet Hung, Nguyen Quoc
dc.date.accessioned2021-03-17T23:53:57Z
dc.date.available2021-03-17T23:53:57Z
dc.date.issued2021
dc.identifier.isbn9781450382977
dc.identifier.doi10.1145/3437963.3441835
dc.identifier.urihttp://hdl.handle.net/10072/403224
dc.description.abstractRecently, 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
dc.description.peerreviewedYes
dc.publisherACM
dc.relation.ispartofconferencenameWSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining
dc.relation.ispartofconferencetitleProceedings of the 14th ACM International Conference on Web Search and Data Mining
dc.relation.ispartofdatefrom2021-03-08
dc.relation.ispartofdateto2021-03-12
dc.relation.ispartoflocationIsrael
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode0801
dc.subject.keywordscs.SI
dc.titleHeterogeneous Hypergraph Embedding for Graph Classification
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationSun, 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
dc.date.updated2021-03-17T23:46:32Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 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
gro.hasfulltextFull Text
gro.griffith.authorNguyen, Henry


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