Results 1 to 10 of about 5,865 (183)

Hypergraph Neural Networks

Proceedings of the AAAI Conference on Artificial Intelligence, 2019
In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure.
Feng, Yifan, Gao, Yue, Ji, Rongrong, You, Haoxuan, Zhang, Zizhao   +4 more
core   +3 more sources

Spectral Detection on Sparse Hypergraphs [PDF]

, 2015
We consider the problem of the assignment of nodes into communities from a set of hyperedges, where every hyperedge is a noisy observation of the community assignment of the adjacent nodes.
Angelini, Maria Chiara, Caltagirone, Francesco, Krzakala, Florent, Zdeborová, Lenka   +3 more
core   +4 more sources

Multi-Order Hypergraph Convolutional Neural Network for Dynamic Social Recommendation System

IEEE Access, 2022
Recently, online social networks have enriched the users’ lives greatly and social recommendation systems make it easier for users to discover more information that they are interested in.
Yu Wang, Qilong Zhao
doaj   +1 more source

Hierarchical Hypergraph-based Attention Neural Network for Service Recommendation [PDF]

Jisuanji kexue
With the rapid growth of various services and APIs on the Internet and the Web,it has become increasingly challenging for developers to quickly and accurately find APIs that meet their needs,thus requiring an efficient recommendation system.Currently,the
YANG Dongsheng, WANG Guiling, ZHENG Xin
doaj   +1 more source

Hyperbolic Directed Hypergraph-Based Reasoning for Multi-Hop KBQA

Mathematics, 2022
The target of the multi-hop knowledge base question-answering task is to find answers of some factoid questions by reasoning across multiple knowledge triples in the knowledge base.
Guanchen Xiao, Jinzhi Liao, Zhen Tan, Yiqi Yu, Bin Ge   +4 more
doaj   +1 more source

Academic team formation as evolving hypergraphs [PDF]

, 2010
This paper quantitatively explores the social and socio-semantic patterns of constitution of academic collaboration teams. To this end, we broadly underline two critical features of social networks of knowledge-based collaboration: first, they ...
A. J. Lott, A.-L. Barabási, B. F. Jones, B. Kogut, B. Latour, B. Uzzi, C. Jones, C. Roth, C. S. Wagner, Camille Roth, Carla Taramasco, D. Constant, D. Crane, D. de Beaver, D. E. Chubin, D. G. Ancona, D. N. Laband, D. Stokols, E. C. M. Noyons, E. Lazega, E. Leahey, G. F. Davis, G. Melin, G. Simmel, I. Karsai, J. D. Adams, J. G. Ruggie, J. J. Ramasco, J. Levrel, J. Moody, J. S. Katz, Jean-Philippe Cointet, K. A. Bollen, L. C. Freeman, M. A. Rodriguez, M. Callon, M. Callon, M. Callon, M. E. J. Newman, M. E. J. Newman, M. McPherson, M. McPherson, M. Ruef, M. Smangs, N. C. Mullins, N. E. Friedkin, P. Haas, P. Larédo, P. Larédo, R. E. Miles, R. Guimera, R. L. Breiger, R. R. Faulkner, R.L. Breiger, S. Wuchty, W. W. Powell   +55 more
core   +6 more sources

Cross-modal Hypergraph Optimisation Learning for Multimodal Sentiment Analysis [PDF]

Jisuanji kexue
Sentiment expressions are multimodal,and more accurate emotions can be derived through multiple modalities such as verbal,audio,and visual.Studying the interactions among modalities can effectively improve the accuracy of multimodal sentiment analysis ...
JIANG Kun, ZHAO Zhengpeng, PU Yuanyuan, HUANG Jian, GU Jinjing, XU Dan
doaj   +1 more source

Consistency of Spectral Hypergraph Partitioning under Planted Partition Model

, 2016
Hypergraph partitioning lies at the heart of a number of problems in machine learning and network sciences. Many algorithms for hypergraph partitioning have been proposed that extend standard approaches for graph partitioning to the case of hypergraphs ...
Dukkipati, Ambedkar, Ghoshdastidar, Debarghya   +1 more
core   +1 more source

Structural Deep Embedding for Hyper-Networks

, 2018
Network embedding has recently attracted lots of attentions in data mining. Existing network embedding methods mainly focus on networks with pairwise relationships. In real world, however, the relationships among data points could go beyond pairwise, i.e.
Cui, Peng, Tu, Ke, Wang, Fei, Wang, Xiao, Zhu, Wenwu   +4 more
core   +1 more source

Multi-Metric Fusion Hypergraph Neural Network for Rotating Machinery Fault Diagnosis

Actuators
Effective fault diagnosis in rotating machinery means extracting fault features from complex samples. However, traditional data-driven methods often overly rely on labeled samples and struggle with extracting high-order complex features. To address these
Jiaxing Zhu, Junlan Hu, Buyun Sheng
doaj   +1 more source