Results 51 to 60 of about 665 (142)

Unifying Invariant and Variant Knowledge With Dual‐Hypergraph Contrastive Evolution for Temporal Knowledge Graph Reasoning

International Journal of Intelligent Systems, Volume 2026, Issue 1, 2026.
Temporal knowledge graph reasoning (TKGR) excels at inferring missing event‐centric facts within a timeline, thereby mitigating the inherent incompleteness of real‐world data. Existing TKGR methods predominantly exploit intrasnapshot structural patterns and intersnapshot temporal dependencies.
Fei Chen, Bing Guo, Zhangtao Cheng, Yan Shen, Mingjie Zhao, Yi Li, Fan Zhou, Richard Murray   +7 more
wiley   +1 more source

Self-Supervised Pretraining for Heterogeneous Hypergraph Neural Networks

, 2023
Recently, pretraining methods for the Graph Neural Networks (GNNs) have been successful at learning effective representations from unlabeled graph data.
Plachouras, Vassilis, Maehara, Takanori, Abubaker, Abdalgader, Nimishakavi, Madhav   +3 more
core  

Temporal Knowledge Graph Reasoning With Historical Data Correlation in Agriculture

International Journal of Intelligent Systems, Volume 2026, Issue 1, 2026.
In terms of temporal dependencies of agricultural data, temporal knowledge graph reasoning is widely used to provide dynamic decision‐making for crop monitoring and disease prediction. Since periodicity, repeatability, and persistence always exist in large, various agricultural data, it is difficult to effectively capture the above important features ...
Qian Luo, Wen Zhang, Jia Wang, Hailong Zhang, Richard Murray   +4 more
wiley   +1 more source

Self-Supervised Hypergraph Representation Learning for Sociological Analysis

, 2023
Modern sociology has profoundly uncovered many convincing social criteria for behavioral analysis. Unfortunately, many of them are too subjective to be measured and very challenging to be presented in online social networks (OSNs) for the large data ...
Sun, Xiangguo, Jia Li, Hongyang Chen, Xiangguo Sun, Cheng, Hong, Xu, Guandong, Li, J, Hong Cheng, Hongzhi Yin, Bo Liu, Yin, H, Liu, Bo, Li, Jia, Chen, Hongyang, Cheng, H, Yin, Hongzhi, Guandong Xu, Liu, B, Xu, G, Sun, X, Chen, H   +20 more
core   +1 more source

Hypergraph neural networks: from signal processing to convolution, u-nets and beyond

Arce, Gonzalo R.Qian, WeiNetwork data has gained significant attention in signal processing and machine learning communities. Existing research mainly centers on simple graphs, which depict only pairwise connections.
Wang, Fuli
core   +1 more source

Hypergraph Convolutional Subspace Clustering With Multihop Aggregation for Hyperspectral Image

, 2022
Subspace clustering methods have become a powerful tool to cluster hyperspectral imaging (HSI) data as they ensure theoretical guarantees and empirical success.
Wenyin Gong, Pedram Ghamisi, Richard Gloaguen, Yaoming Cai, Xiaobo Liu, Zijia Zhang   +5 more
core   +1 more source

Beyond Hypergraph Dualization

, 2016
International audienceThis problem concerns hypergraph dualization and generalization to poset dualization. A hypergraph H = (V, E) consists of a finite collection E of sets over a finite set V , i.e. E ⊆ P(V) (the powerset of V).
Petit, Jean-Marc, Nourine, Lhouari, Jean-Marc Petit, Lhouari Nourine   +3 more
core   +1 more source

Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning

, 2023
With the proliferation of social media, a growing number of users search for and join group activities in their daily life. This develops a need for the study on the group identification (GI) task, i.e., recommending groups to users.
Yang, Mingdai, Yang, Liangwei, Peng, Hao, Yu, Philip S., Liu, Xiaolong, Liu, Zhiwei, Wang, Chen   +6 more
core   +1 more source

Unified Low-Rank Subspace Clustering with Dynamic Hypergraph for Hyperspectral Image

, 2021
Low-rank representation with hypergraph regularization has achieved great success in hyperspectral imagery, which can explore global structure, and further incorporate local information.
Jingxiang Yang, Jinhuan Xu, Liang Xiao
core   +1 more source

Self-complementing permutations of k-uniform hypergraphs [PDF]

, 2009
Graphs and AlgorithmsInternational audienceA k-uniform hypergraph H = ( V; E) is said to be self-complementary whenever it is isomorphic with its complement (H) over bar = ( V; ((V)(k)) - E).
Adam Pawel Wojda, Wojda, Adam Pawel, Szymański, Artur, Artur Szymański   +3 more
core   +1 more source