Enhancing Hyperbolic Graph Embeddings via Contrastive Learning
Harbin Institute of Technology(Shenzhen)（哈尔滨工业大学（深圳））；The Chinese University of Hong Kong（香港中文大学）；Huawei Technologies co.ltd（华为技术有限公司）；Shenzhen University（深圳大学）
Jiahong Liu, Menglin Yang, Min Zhou, Shanshan Feng, Philippe Fournier-Viger
备注：
Accepted by NeurIPS'21@2nd Workshop on Self-Supervised Learning
Recently, hyperbolic space has risen as a promising alternative for semi-supervised graph representation learning. Many efforts have been made to design hyperbolic versions of neural network operations. However, the inspiring geometric properties of this unique geometry have not been fully explored yet. The potency of graph models powered by the hyperbolic space is still largely underestimated. Besides, the rich information carried by abundant unlabelled samples is also not well utilized. Inspired by the recently active and emerging self-supervised learning, in this study, we attempt to enhance the representation power of hyperbolic graph models by drawing upon the advantages of contrastive learning. More specifically, we put forward a novel Hyperbolic Graph Contrastive Learning (HGCL) framework which learns node representations through multiple hyperbolic spaces to implicitly capture the hierarchical structure shared between different views. Then, we design a hyperbolic position consistency (HPC) constraint based on hyperbolic distance and the homophily assumption to make contrastive learning fit into hyperbolic space. Experimental results on multiple real-world datasets demonstrate the superiority of the proposed HGCL as it consistently outperforms competing methods by considerable margins for the node classification task.
Learning good image representations that are beneficial to downstream tasks is a challenging task in computer vision. As such, a wide variety of self-supervised learning approaches have been proposed. Among them, contrastive learning has shown competitive performance on several benchmark datasets. The embeddings of contrastive learning are arranged on a hypersphere that results in using the inner (dot) product as a distance measurement in Euclidean space. However, the underlying structure of many scientific fields like social networks, brain imaging, and computer graphics data exhibit highly n…
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）
FairContrast: Enhancing Fairness through Contrastive learning and Customized Augmenting Methods on Tabular Data
Department of Industrial Engineering University of Central Florida（工业工程系，中央佛罗里达大学）；Department of Computer Science University of Central Florida（计算机科学系，中央佛罗里达大学）
University of Maryland, College Park（马里兰大学帕克分校）；Google Research（谷歌研究院）；Meta
Although self-/un-supervised methods have led to rapid progress in visual representation learning, these methods generally treat objects and scenes using the same lens. In this paper, we focus on learning representations for objects and scenes that preserve the structure among them. Motivated by the observation that visually similar objects are close in the representation space, we argue that the scenes and objects should instead follow a hierarchical structure based on their compositionality. To exploit such a structure, we propose a contrastive learning framework where a Euclidean loss is us…
Machine Unlearning in Hyperbolic vs. Euclidean Multimodal Contrastive Learning: Adapting Alignment Calibration to MERU
Aalborg University（奥尔堡大学）；University of Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）；Milestone Systems（里程碑系统公司）；Pioneer Center for Artificial Intelligence（先锋人工智能中心）
Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach
Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）；ByteDance（字节跳动）；National Institute of Health Data Science, Peking University（北京大学国家健康数据科学研究院）
Graph Neural Networks (GNNs) have achieved great success in learning graph representations and thus facilitating various graph-related tasks. However, most GNN methods adopt a supervised learning setting, which is not always feasible in real-world applications due to the difficulty to obtain labeled data. Hence, graph self-supervised learning has been attracting increasing attention. Graph contrastive learning (GCL) is a representative framework for self-supervised learning. In general, GCL learns node representations by contrasting semantically similar nodes (positive samples) and dissimilar …
ASIL: Augmented Structural Information Learning for Deep Graph Clustering in Hyperbolic Space
Beijing University of Posts and Telecommunications（北京邮电大学）；North China Electric Power University（华北电力大学）；DiDi Chuxing（滴滴出行）；Beihang University（北航）；University of Illinois at Chicago（伊利诺伊大学香槟分校）
Enhancing Text-Based Hierarchical Multilabel Classification for Mobile Applications via Contrastive Learning
Tencent Shenzhen Guangdong China；Xidian University Xi'an Shaanxi China；Shenzhen Institute of Information Technology Shenzhen Guangdong China；Independent Researcher Dublin County Dublin Ireland；Tencent；Xidian University；Shenzhen Institute of Information Technology；Independent Researcher
Does GCL Need a Large Number of Negative Samples? Enhancing Graph Contrastive Learning with Effective and Efficient Negative Sampling
College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；Department of Data Science, George Washington University（乔治华盛顿大学数据科学系）；School of Cybersecurity, Northwestern Polytechnical University（西北工业大学网络空间安全学院）
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；Baidu Inc.（百度公司）；Griffith University（格里菲斯大学）；Xiaomi AI Lab（小米人工智能实验室）
Self-supervised learning (especially contrastive learning) methods on heterogeneous graphs can effectively get rid of the dependence on supervisory data. Meanwhile, most existing representation learning methods embed the heterogeneous graphs into a single geometric space, either Euclidean or hyperbolic. This kind of single geometric view is usually not enough to observe the complete picture of heterogeneous graphs due to their rich semantics and complex structures. Under these observations, this paper proposes a novel self-supervised learning method, termed as Geometry Contrastive Learning (GC…
Enhancing IMU-Based Online Handwriting Recognition via Contrastive Learning with Zero Inference Overhead
Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-艾萨克-亚历山大大学埃尔朗根-纽伦堡）；STABILO International GmbH（STABILO国际有限公司）；Ludwig-Maximilians-Universität München（路德维希-马克西米利安大学慕尼黑）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Helmholtz Zentrum München - German Research Center for Environmental Health（海德堡中心慕尼黑 - 德国环境健康研究中心）
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization
National University of Singapore（新加坡国立大学）；RIKEN Center for Advanced Intelligence Project (AIP)（理化学研究所先进智能项目中心）；The University of Auckland（奥克兰大学）；The University of Melbourne（墨尔本大学）；The University of Tokyo（东京大学）
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；US Army Research Laboratory（美国陆军研究实验室）
This paper presents a semi-supervised learning framework that is new in being designed for automatic modulation classification (AMC). By carefully utilizing unlabeled signal data with a self-supervised contrastive-learning pre-training step, our framework achieves higher performance given smaller amounts of labeled data, thereby largely reducing the labeling burden of deep learning. We evaluate the performance of our semi-supervised framework on a public dataset. The evaluation results demonstrate that our semi-supervised approach significantly outperforms supervised frameworks thereby substan…
Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization
The Ohio State University（俄亥俄州立大学）；Biomedical Informatics Department, The Ohio State University（俄亥俄州立大学生物医学信息学系）；Computer Science and Engineering Department, The Ohio State University（俄亥俄州立大学计算机科学与工程系）；Translational Data Analytics Institute, The Ohio State University（俄亥俄州立大学转化数据分析研究所）
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；University of Trento（特伦托大学）；Zhuhai College of Science and Technology（珠海科技学院）
Tensor-DTI: Enhancing Biomolecular Interaction Prediction with Contrastive Embedding Learning
Nostrum Biodiscovery；Barcelona Supercomputing Center（巴塞罗那超级计算中心）；Faculty of Pharmacy and Food Sciences, University of Barcelona（巴塞罗那大学药学与食品科学系）；Catalan Institution for Research and Advanced Studies (ICREA)（加泰罗尼亚研究与高级研究机构）；Data Science Dpt., Almirall S.A.（Almirall S.A.数据科学部门）
Xidian University（西安电子科技大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
Embedded topic models are able to learn interpretable topics even with large and heavy-tailed vocabularies. However, they generally hold the Euclidean embedding space assumption, leading to a basic limitation in capturing hierarchical relations. To this end, we present a novel framework that introduces hyperbolic embeddings to represent words and topics. With the tree-likeness property of hyperbolic space, the underlying semantic hierarchy among words and topics can be better exploited to mine more interpretable topics. Furthermore, due to the superiority of hyperbolic geometry in representing…
Seoul National University（首尔国立大学）；Hyundai Motor Group（现代汽车集团）
Enhancing feature transferability by matching marginal distributions has led to improvements in domain adaptation, although this is at the expense of feature discrimination. In particular, the ideal joint hypothesis error in the target error upper bound, which was previously considered to be minute, has been found to be significant, impairing its theoretical guarantee. In this paper, we propose an alternative upper bound on the target error that explicitly considers the joint error to render it more manageable. With the theoretical analysis, we suggest a joint optimization framework that combi…
University of Technology Sydney（悉尼科技大学）；Monash University（莫纳什大学）；Wuhan University of Technology（武汉理工大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）
Unsupervised graph representation learning has emerged as a powerful tool to address real-world problems and achieves huge success in the graph learning domain. Graph contrastive learning is one of the unsupervised graph representation learning methods, which recently attracts attention from researchers and has achieved state-of-the-art performances on various tasks. The key to the success of graph contrastive learning is to construct proper contrasting pairs to acquire the underlying structural semantics of the graph. However, this key part is not fully explored currently, most of the ways ge…
CSP-AIT-Net: A contrastive learning-enhanced spatiotemporal graph attention framework for short-term metro OD flow prediction with asynchronous inflow tracking
Hyperbolic Multimodal Representation Learning for Biological Taxonomies
Simon Fraser University（西蒙弗雷泽大学）；University of Waterloo（滑铁卢大学）；Vector Institute（向量研究所）；University of Guelph（圭尔夫大学）；Alberta Machine Intelligence Institute (Amii)（阿尔伯塔机器智能研究所）；Aalborg University（奥胡斯大学）
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Tencent（腾讯）
Recently, Graph Neural Networks (GNNs) achieve remarkable success in Recommendation. To reduce the influence of data sparsity, Graph Contrastive Learning (GCL) is adopted in GNN-based CF methods for enhancing performance. Most GCL methods consist of data augmentation and contrastive loss (e.g., InfoNCE). GCL methods construct the contrastive pairs by hand-crafted graph augmentations and maximize the agreement between different views of the same node compared to that of other nodes, which is known as the InfoMax principle. However, improper data augmentation will hinder the performance of GCL. …
University of Electronic Science and Technology of China（电子科技大学）；Tencent Inc.（腾讯公司）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Peng Cheng National Lab（鹏城国家实验室）
Hierarchical clustering recursively partitions data at an increasingly finer granularity. In real-world applications, multi-view data have become increasingly important. This raises a less investigated problem, i.e., multi-view hierarchical clustering, to better understand the hierarchical structure of multi-view data. To this end, we propose a novel neural network-based model, namely Contrastive Multi-view Hyperbolic Hierarchical Clustering (CMHHC). It consists of three components, i.e., multi-view alignment learning, aligned feature similarity learning, and continuous hyperbolic hierarchical…
