Contrastive learning has demonstrated strong performance in attributed hypergraph clustering. Typically, existing methods based on contrastive learning first learn node embeddings and then apply clustering algorithms, such as k-means, to these embeddings to obtain the clustering results.However, these methods lack direct clustering supervision, risking the inclusion of clustering-irrelevant information in the learned graph.To this end, we propose a Contrastive learning approach for Attributed Hypergraph Clustering (CAHC), an end-to-end method that simultaneously learns node embeddings and obtains clustering results. CAHC consists of two main steps: representation learning and cluster assignment learning. The former employs a novel contrastive learning approach that incorporates both node-level and hyperedge-level objectives to generate node embeddings.The latter joint embedding and clustering optimization to refine these embeddings by clustering-oriented guidance and obtains clustering results simultaneously.Extensive experimental results demonstrate that CAHC outperforms baselines on eight datasets.
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…
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）
HyperFuse: Fast Self-Supervised Node Embeddings for Attributed Hypergraphs
Indian Institute of Science Education and Research Thiruvananthapuram（印度科学教育与研究学院蒂鲁文南特普拉姆分校）；Indian Institute of Science Education and Research Kolkata（印度科学教育与研究学院加尔各答分校）；University of Rostock（罗斯托克大学）；Stellenbosch Institute for Advanced Study (STIAS)（斯泰伦博斯高等研究院）
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…
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）
Hybrid-Collaborative Augmentation and Contrastive Sample Adaptive-Differential Awareness for Robust Attributed Graph Clustering
College of Information Science and Technology, Beijing University of Chemical Technology（北京化工大学信息科学与技术学院）；Beijing University of Technology（北京工业大学）；The University of Sydney Business School, The University of Sydney（悉尼大学商学院）
Monash University（莫纳什大学）；Alibaba Group（阿里巴巴集团）；Nanjing University of Science and Technology（南京理工大学）；The Hong Kong Polytechnic University（香港理工大学）；Chinese Academy of Sciences（中国科学院）；University of Minnesota（明尼苏达大学）
Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recently, the deep learning-based anomaly detection methods have shown promising results over shallow approaches, especially on networks with high-dimensional attributes and complex structures. However, existing approaches, which employ graph autoencoder as their backbone, do not fully exploit the rich information of the network, resulting in suboptimal performance. Furthermore, these methods do not directly target anomal…
Text-Attributed Graph Anomaly Detection via Multi-Scale Cross- and Uni-Modal Contrastive Learning
School of Computer Science and Technology, Xi'an Jiaotong University（西安交通大学计算机科学与技术学院）；Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering, Xi’an Jiaotong University（陕西省大数据知识工程重点实验室）；School of Distance Education, Xi’an Jiaotong University（西安交通大学继续教育学院）；University of Virginia（弗吉尼亚大学）
SCALA: Sparsification-based Contrastive Learning for Anomaly Detection on Attributed Networks
College of Computer Science and Technology, Harbin Engineering University（哈尔滨工程大学计算机科学与技术学院）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）
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）
Grid AI Labs（Grid人工智能实验室）；New York University（纽约大学）
Recent methods for self-supervised learning can be grouped into two paradigms: contrastive and non-contrastive approaches. Their success can largely be attributed to data augmentation pipelines which generate multiple views of a single input that preserve the underlying semantics. In this work, we introduce augmentation-augmented stochastic autoencoders (AASAE), yet another alternative to self-supervised learning, based on autoencoding. We derive AASAE starting from the conventional variational autoencoder (VAE), by replacing the KL divergence regularization, which is agnostic to the input dom…
Self-supervised contrastive learning performs non-linear system identification
Institute of Computational Biology, Computational Health Center, Helmholtz Munich and Munich Center for Machine Learning (MCML)（计算生物学研究所、计算健康中心、海德堡慕尼黑和慕尼黑机器学习中心）
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）
Computer-aided breast cancer diagnosis in mammography is a challenging problem, stemming from mammographical data scarcity and data entanglement. In particular, data scarcity is attributed to the privacy and expensive annotation. And data entanglement is due to the high similarity between benign and malignant masses, of which manifolds reside in lower dimensional space with very small margin. To address these two challenges, we propose a deep learning framework, named Contrastive Identifier Network (\textsc{COIN}), which integrates adversarial augmentation and manifold-based contrastive learni…
It has been verified that only a small fraction of the neoantigens presented by MHC class I molecules on the cell surface can elicit T cells. The limitation can be attributed to the binding specificity of T cell receptor (TCR) to peptide-MHC complex (pMHC). Computational prediction of T cell binding to neoantigens is an challenging and unresolved task. In this paper, we propose an attentive-mask contrastive learning model, ATMTCR, for inferring TCR-antigen binding specificity. For each input TCR sequence, we used Transformer encoder to transform it to latent representation, and then masked a p…
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）
University of Electronic Science and Technology of China（电子科技大学）
With the explosive growth of information technology, multi-view graph data have become increasingly prevalent and valuable. Most existing multi-view clustering techniques either focus on the scenario of multiple graphs or multi-view attributes. In this paper, we propose a generic framework to cluster multi-view attributed graph data. Specifically, inspired by the success of contrastive learning, we propose multi-view contrastive graph clustering (MCGC) method to learn a consensus graph since the original graph could be noisy or incomplete and is not directly applicable. Our method composes of …
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）
Inference and Visualization of Community Structure in Attributed Hypergraphs Using Mixed-Membership Stochastic Block Models
Graduate School of Systems Design, Tokyo Metropolitan University（东京 Metropolitan 大学系统设计研究生院）；Principles of Informatics Research Division, National Institute of Informatics（国家信息研究所信息原理研究部门）
Advertising click fraud detection plays one of the vital roles in current E-commerce websites as advertising is an essential component of its business model. It aims at, given a set of corresponding features, e.g., demographic information of users and statistical features of clicks, predicting whether a click is fraudulent or not in the community. Recent efforts attempted to incorporate attributed behavior sequence and heterogeneous network for extracting complex features of users and achieved significant effects on click fraud detection. In this paper, we propose a Multimodal and Contrastive …
Pre-trained sequence-to-sequence language models have led to widespread success in many natural language generation tasks. However, there has been relatively less work on analyzing their ability to generate structured outputs such as graphs. Unlike natural language, graphs have distinct structural and semantic properties in the context of a downstream NLP task, e.g., generating a graph that is connected and acyclic can be attributed to its structural constraints, while the semantics of a graph can refer to how meaningfully an edge represents the relation between two node concepts. In this work…
University of Chinese Academy of Sciences（中国科学院大学）；Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences（中国科学院大学宁波生命与健康产业研究院）；The University of Hong Kong（香港大学）；Xidian University（西安电子科技大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
\emph{Objective and Impact Statement}. With the renaissance of deep learning, automatic diagnostic systems for computed tomography (CT) have achieved many successful applications. However, they are mostly attributed to careful expert annotations, which are often scarce in practice. This drives our interest to the unsupervised representation learning. \emph{Introduction}. Recent studies have shown that self-supervised learning is an effective approach for learning representations, but most of them rely on the empirical design of transformations and pretext tasks. \emph{Methods}. To avoid the su…
Boosting Object Representation Learning via Motion and Object Continuity
TU Darmstadt（达姆施塔特工业大学）；Hessian Center for AI (hessian.AI)（黑森人工智能中心）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Centre for Cognitive Science, TU Darmstadt（达姆施塔特工业大学认知科学中心）
ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness
National University of Defense Technology（国防科技大学）；Chongqing University of Posts and Telecommunications（重庆邮电大学）；Intelligent Game and Decision Laboratory（智能博弈与决策实验室）
