Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks Article Swipe
Hansheng Xue
,
Luwei Yang
,
Vaibhav Rajan
,
Wen Jiang
,
Wei Yi
,
Yu Lin
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2102.06371
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2102.06371
A bipartite network is a graph structure where nodes are from two distinct domains and only inter-domain interactions exist as edges. A large number of network embedding methods exist to learn vectorial node representations from general graphs with both homogeneous and heterogeneous node and edge types, including some that can specifically model the distinct properties of bipartite networks. However, these methods are inadequate to model multiplex bipartite networks (e.g., in e-commerce), that have multiple types of interactions (e.g., click, inquiry, and buy) and node attributes. Most real-world multiplex bipartite networks are also sparse and have imbalanced node distributions that are challenging to model. In this paper, we develop an unsupervised Dual HyperGraph Convolutional Network (DualHGCN) model that scalably transforms the multiplex bipartite network into two sets of homogeneous hypergraphs and uses spectral hypergraph convolutional operators, along with intra- and inter-message passing strategies to promote information exchange within and across domains, to learn effective node embedding. We benchmark DualHGCN using four real-world datasets on link prediction and node classification tasks. Our extensive experiments demonstrate that DualHGCN significantly outperforms state-of-the-art methods, and is robust to varying sparsity levels and imbalanced node distributions.
Related Topics
Concepts
Bipartite graph
Embedding
Hypergraph
Computer science
Node (physics)
Theoretical computer science
Link (geometry)
Benchmark (surveying)
Dual (grammatical number)
Graph
Enhanced Data Rates for GSM Evolution
Artificial intelligence
Mathematics
Computer network
Combinatorics
Literature
Art
Geodesy
Structural engineering
Geography
Engineering
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2102.06371
- https://arxiv.org/pdf/2102.06371
- OA Status
- green
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3130605614
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3130605614Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2102.06371Digital Object Identifier
- Title
-
Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional NetworksWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-02-12Full publication date if available
- Authors
-
Hansheng Xue, Luwei Yang, Vaibhav Rajan, Wen Jiang, Wei Yi, Yu LinList of authors in order
- Landing page
-
https://arxiv.org/abs/2102.06371Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2102.06371Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2102.06371Direct OA link when available
- Concepts
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Bipartite graph, Embedding, Hypergraph, Computer science, Node (physics), Theoretical computer science, Link (geometry), Benchmark (surveying), Dual (grammatical number), Graph, Enhanced Data Rates for GSM Evolution, Artificial intelligence, Mathematics, Computer network, Combinatorics, Literature, Art, Geodesy, Structural engineering, Geography, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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44Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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