通知：联邦节假日调休：哥伦布日 / 原住民日（Columbus Day / Indigenous Peoples' Day），今日（13日）无更新。通知：联邦节假日调休：哥伦布日 / 原住民日（Columbus Day / Indigenous Peoples' Day），今日（13日）无更新。
arXiv 2303.06213cs.LGcs.AI
CHGNN：一种半监督对比超图学习网络
CHGNN: A Semi-Supervised Contrastive Hypergraph Learning Network
School of Computer Science and Engineering, Northeastern University（东北大学计算机科学与工程学院）；Aalborg University（奥尔堡大学）；School of Computing and Information Systems, The University of Melbourne（墨尔本大学计算与信息系统学院）
Yumeng Song, Yu Gu, Tianyi Li, Jianzhong Qi, Zhenghao Liu, Christian S. Jensen, Ge Yu
Hypergraphs can model higher-order relationships among data objects that are found in applications such as social networks and bioinformatics. However, recent studies on hypergraph learning that extend graph convolutional networks to hypergraphs cannot learn effectively from features of unlabeled data. To such learning, we propose a contrastive hypergraph neural network, CHGNN, that exploits self-supervised contrastive learning techniques to learn from labeled and unlabeled data. First, CHGNN includes an adaptive hypergraph view generator that adopts an auto-augmentation strategy and learns a perturbed probability distribution of minimal sufficient views. Second, CHGNN encompasses an improved hypergraph encoder that considers hyperedge homogeneity to fuse information effectively. Third, CHGNN is equipped with a joint loss function that combines a similarity loss for the view generator, a node classification loss, and a hyperedge homogeneity loss to inject supervision signals. It also includes basic and cross-validation contrastive losses, associated with an enhanced contrastive loss training process. Experimental results on nine real datasets offer insight into the effectiveness of CHGNN, showing that it outperforms 13 competitors in terms of classification accuracy consistently.
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Texas A&M University（德州农工大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
This 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). We focus on the following question: How to construct contrastive views for hypergraphs via augmentations? We provide the solutions in two folds. First, guided by domain knowledge, we fabricate two schemes to augment hyperedges with higher-order relations encoded, and adopt three vertex augmentation strategies from graph-structured data. Second, in search of more effective views in a data-d…
Closer through commonality: Enhancing hypergraph contrastive learning with shared groups
Graduate School of Data Science, KAIST（韩国科学技术院数据科学研究生院）；School of Computer Engineering, Hansung University（韩星大学计算机工程学院）；Department of Industrial and Systems Engineering, KAIST（韩国科学技术院工业与系统工程系）
Although machine learning on hypergraphs has attracted considerable attention, most of the works have focused on (semi-)supervised learning, which may cause heavy labeling costs and poor generalization. Recently, contrastive learning has emerged as a successful unsupervised representation learning method. Despite the prosperous development of contrastive learning in other domains, contrastive learning on hypergraphs remains little explored. In this paper, we propose TriCL (Tri-directional Contrastive Learning), a general framework for contrastive learning on hypergraphs. Its main idea is tri-d…
University of Hong Kong（香港大学）；South China University of Technology（华南理工大学）；Wilfrid Laurier University（威尔弗里德·劳雷尔大学）；Baidu Inc.（百度公司）；York University（约克大学）
Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing users and items into latent representation space, with their correlative patterns from interaction data. Among various CF techniques, the development of GNN-based recommender systems, e.g., PinSage and LightGCN, has offered the state-of-the-art performance. However, two key challenges have not been well explored in existing solutions: i) The over-smoothing effect with deeper graph-based CF architecture, may cause the indistinguishable user representations and degradation of recommendation results. ii) The supe…
Hypergraph Contrastive Learning for both Homophilic and Heterophilic Hypergraphs
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；School of Mathematical and Computer Sciences, Heriot-Watt University（赫瑞斯泰大学数学与计算机科学学院）；Department of Information Engineering and Computer Science, University of Trento（特伦托大学信息工程与计算机科学系）
MMHCL: Multi-Modal Hypergraph Contrastive Learning for Recommendation
School of Computer Science and Engineering, Nanjing University of Science and Technology（计算机科学与工程学院，南京理工大学）；Shituoyun (Nanjing) Technology Co., Ltd（石墨云（南京）科技有限公司）；School of Artificial Intelligence, Beijing Normal University（人工智能学院，北京师范大学）
H3Former: Hypergraph-based Semantic-Aware Aggregation via Hyperbolic Hierarchical Contrastive Loss for Fine-Grained Visual Classification
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；College of Software, Jilin University（吉林大学软件学院）；School of Software, Tsinghua University（清华大学软件学院）
DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge Graphs
Jiangsu Key Laboratory of Networked Collective Intelligence, School of Mathematics, Southeast University（江苏网络集体智能重点实验室，数学学院，东南大学）；Jiangsu Key Laboratory of Networked Collective Intelligence, School of Cyber Science and Engineering, Southeast University（江苏网络集体智能重点实验室，网络科学与工程学院，东南大学）；State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, University of Chinese Academy of Sciences（数学科学国家重点实验室，数学与系统科学研究院，中国科学院大学）
School of Cyberspace, Hangzhou Dianzi University（杭州电子科技大学信息学院）；Data Security Governance Zhejiang Engineering Research Center（浙江数据安全治理工程研究中心）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；College of Intelligence and Computing, Tianjin University（天津大学智能与计算学院）
School of Computing, Macquarie University（麦考瑞大学计算学院）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Department of Computer Science and Engineering, Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Peng Cheng Laboratory（鹏城实验室）；Sun Yat-sen University（中山大学）；Chongqing University（重庆大学）；Shanghai Pudong Development Bank（上海浦东发展银行）
Emotion Collider: Dual Hyperbolic Mirror Manifolds for Sentiment Recovery via Anti Emotion Reflection
University of Macau（澳门大学）；Zhejiang University（浙江大学）；Tsinghua University（清华大学）；Tongji University（同济大学）；University of Southampton（南安普顿大学）；University of Bologna（博洛尼亚大学）；Minzu University of China（中央民族大学）
University of Cambridge（剑桥大学）；Universite de Bordeaux（波尔多大学）
The automatic early diagnosis of prodromal stages of Alzheimer's disease is of great relevance for patient treatment to improve quality of life. We address this problem as a multi-modal classification task. Multi-modal data provides richer and complementary information. However, existing techniques only consider either lower order relations between the data and single/multi-modal imaging data. In this work, we introduce a novel semi-supervised hypergraph learning framework for Alzheimer's disease diagnosis. Our framework allows for higher-order relations among multi-modal imaging and non-imagi…
