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HGNN+: General Hypergraph Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022Graph Neural Networks have attracted increasing attention in recent years. However, existing GNN frameworks are deployed based upon simple graphs, which limits their applications in dealing with complex data correlation of multi-modal/multi-type data in ...
Yue Gao +3 more
semanticscholar +1 more source
The Web Conference, 2023
Knowledge hypergraph embedding, which projects entities and n-ary relations into a low-dimensional continuous vector space to predict missing links, remains a challenging area to be explored despite the ubiquity of n-ary relational facts in the real ...
Chenxu Wang +4 more
semanticscholar +1 more source
Knowledge hypergraph embedding, which projects entities and n-ary relations into a low-dimensional continuous vector space to predict missing links, remains a challenging area to be explored despite the ubiquity of n-ary relational facts in the real ...
Chenxu Wang +4 more
semanticscholar +1 more source
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
This paper delves into the problem of correlated time-series forecasting in practical applications, an area of growing interest in a multitude of fields such as stock price prediction and traffic demand analysis. Current methodologies primarily represent
Nan Yin +7 more
semanticscholar +1 more source
This paper delves into the problem of correlated time-series forecasting in practical applications, an area of growing interest in a multitude of fields such as stock price prediction and traffic demand analysis. Current methodologies primarily represent
Nan Yin +7 more
semanticscholar +1 more source
Hypergraph Similarity Measures
IEEE Transactions on Network Science and Engineering, 2023In this paper we present a novel framework for hypergraph similarity measures (HSMs) for hypergraph comparison. Hypergraphs are generalizations of graphs in which edges may connect any number of vertices, thereby representing multi-way relationships ...
Amit Surana, Can Chen, I. Rajapakse
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A Universal Quaternion Hypergraph Network for Multimodal Video Question Answering
IEEE transactions on multimedia, 2023Fusion and interaction of multimodal features are essential for video question answering. Structural information composed of the relationships between different objects in videos is very complex, which restricts understanding and reasoning. In this paper,
Zhicheng Guo +4 more
semanticscholar +1 more source
IEEE Transactions on Instrumentation and Measurement, 2023
Timely and accurate fault diagnosis plays a critical role in today’s smart manufacturing practices, saving invaluable time and expenditure on maintenance process. To date, numerous data-driven approaches have been introduced for equipment fault diagnosis,
Liqiao Xia +3 more
semanticscholar +1 more source
Timely and accurate fault diagnosis plays a critical role in today’s smart manufacturing practices, saving invaluable time and expenditure on maintenance process. To date, numerous data-driven approaches have been introduced for equipment fault diagnosis,
Liqiao Xia +3 more
semanticscholar +1 more source
Multimodal Remote Sensing Image Segmentation With Intuition-Inspired Hypergraph Modeling
IEEE Transactions on Image Processing, 2023Multimodal remote sensing (RS) image segmentation aims to comprehensively utilize multiple RS modalities to assign pixel-level semantics to the studied scenes, which can provide a new perspective for global city understanding.
Qi He +5 more
semanticscholar +1 more source
Hypergraph Learning: Methods and Practices
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020Hypergraph learning is a technique for conducting learning on a hypergraph structure. In recent years, hypergraph learning has attracted increasing attention due to its flexibility and capability in modeling complex data correlation.
Yue Gao +5 more
semanticscholar +1 more source
t-HGSP: Hypergraph Signal Processing Using t-Product Tensor Decompositions
IEEE Transactions on Signal and Information Processing over Networks, 2023Graph signal processing (GSP) techniques are powerful tools that model complex relationships within large datasets, being now used in a myriad of applications in different areas including data science, communication networks, epidemiology, and sociology.
K. Pena-Pena, D. Lau, G. Arce
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Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative
Neural Information Processing Systems, 2022This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (we refer to it as HyperGCL).
Tianxin Wei +5 more
semanticscholar +1 more source