Results 61 to 70 of about 665 (142)

xiaxin1998/DHCN: Codes for paper 'Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation'

, 2020
Codes for paper 'Self-Supervised Hypergraph Convolutional Networks for Session-based ...
Zhang, Xiangliang, Wang, Qinyong, Cui, Lizhen, Yu, Junliang, Yin, Hongzhi, Xia, Xin   +5 more
core  

Self-Supervised Hypergraph Learning for Enhanced Multimodal Representation

Hypergraph neural networks have gained substantial popularity in capturing complex correlations between data items in multimodal datasets. In this study, we propose a novel approach called the self-supervised hypergraph learning (SHL) framework that ...
Chaojun Meng, Jia Zhu, Pasquale De Meo, Qing Wang, Hongji Shu   +4 more
core   +1 more source

A note of self-complementary hypergraphs

, 2005
In the paper we describe all self-complementary hypergraphs. It turns out that such hypergraphs exist if and only if the number of vertices of the hypergraph is of the form n = 2k. This answers a conjecture posed by A. Szymański (see[3])
Zwonek, M.
core  

Self-supervised Guided Hypergraph Feature Propagation for Semi-supervised Classification with Missing Node Features

, 2023
Graph neural networks (GNNs) with missing node features have recently received increasing interest. Such missing node features seriously hurt the performance of the existing GNNs.
Qiu, Wenhao, Fu, Sichao, Hu, Yachen, Peng, Qinmu, Wang, Yuetian, You, Xinge, Lei, Chengxiang   +6 more
core  

Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph

, 2023
Knowledge graphs (KGs) are commonly used as side information to enhance collaborative signals and improve recommendation quality. In the context of knowledge-aware recommendation (KGR), graph neural networks (GNNs) have emerged as promising solutions for
Liu, Yi, Li, Bohan, Yin, Hongzhi, Xuan, Hongrui, Wang, Meng, Chen, Tong   +5 more
core  

Application of Hypergraph-Driven "Trinity" Semantic Framework in Retail Commodity Detection

物联网学报
The efficiency of retail checkout systems directly affects the shopping experience. Current systems have difficulty capturing key details of Small-sized merchandise and lack spatial reasoning in crowded scenes, which limits detection performance.
CAO ZHIGANG, LIU LIBO, LIU QIAN, DENG ZHEN   +3 more
doaj  

Cyclically t-complementary uniform hypergraphs

, 2010
A cyclically t-complementary k-hypergraph is a k-uniform hypergraph with vertex set V and edge set E for which there exists a permutation θ∈Sym(V) such that the sets E,Eθ,Eθ2,…,Eθt−1 partition the set of all k-subsets of V. Such a permutation θ is called
Gosselin, Shonda, Shonda Gosselin
core   +1 more source

Application of hypergraphs in decomposition of discrete systems

, 2012
seria: Lecture Notes in Control and Computer Science ; vol ...
Wiśniewska, Monika
core  

Self-Supervised Knowledge-Aware Recommendation Model Integrating Adaptive Hypergraph [PDF]

To alleviate the cold-start problem that exists in traditional collaborative filtering recommendation systems, knowledge graphs have been introduced as a kind of auxiliary knowledge in recommendation systems.
ZHOU Jiaxuan, LIU Xianhui, ZHAO Xiaodong, HOU Wenlong, ZHAO Weidong
core   +1 more source

Higher-order knowledge-enhanced recommendation with heterogeneous hypergraph multi-attention

Recent advancements in recommender systems have focused on integrating knowledge graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced recommenders is to incorporate rich semantic information for more accurate recommendations.
Sakong, D, Vu, VH, Nguyen, PL, Huynh, TT, Nguyen, TT, Yin, H, Nguyen, QVH   +6 more
core   +1 more source