gzcsudo / awesome-hypergraph-network

A curated list of Hypergraph Learning, Hypergraph Theory, Hypergraph Dataset and Hypergraph Tool.

Hypergraph Survey

• Hypergraph Learning: Methods and Practices (TPAMI, 2022) [paper]
• More Recent Advances in (Hyper)Graph Partitioning (ACM Computing Surveys, 2022) [paper]
• A Survey on Hypergraph Representation Learning (ACM Computing Surveys, 2023) [paper]

Hypergraph Learning

Conference Papers

International Conference on Machine Learning

• Exact Inference in High-order Structured Prediction (ICML, 2023) [paper]
• From Hypergraph Energy Functions to Hypergraph Neural Networks (ICML, 2023) [paper]
• Optimal LP Rounding and Linear-Time Approximation Algorithms for Clustering Edge-Colored Hypergraphs (ICML, 2023) [paper]
• Projected Tensor Power Method for Hypergraph Community Recovery (ICML, 2023) [paper]
• Nonlinear Feature Diffusion on Hypergraphs (ICML, 2022) [paper]
• Two Heads are Better Than One: Hypergraph-Enhanced Graph Reasoning for Visual Event Ratiocination (ICML, 2021) [paper]
• Random Walks on Hypergraphs with Edge-Dependent Vertex Weights (ICML, 2019) [paper]
• Molecular Hypergraph Grammar with Its Application to Molecular Optimization (ICML, 2019) [paper]
• Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering (ICML, 2018) [paper]
• Re-revisiting Learning on Hypergraphs: Confidence Interval and Subgradient Method (ICML, 2017) [paper]

Annual Conference on Neural Information Processing Systems

• CAT-Walk: Inductive Hypergraph Learning via Set Walks (NeurIPS, 2023) [paper]
• HyTrel: Hypergraph-enhanced Tabular Data Representation Learning (NeurIPS, 2023) [paper]
• Sheaf Hypergraph Networks (NeurIPS, 2023) [paper]
• SHINE: SubHypergraph Inductive Neural nEtwork (NeurIPS, 2022) [paper]
• Sparse Hypergraph Community Detection Thresholds in Stochastic Block Model (NeurIPS, 2022) [paper]
• Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative (NeurIPS, 2022) [paper]
• Hypergraph Propagation and Community Selection for Objects Retrieval (NeurIPS, 2021) [paper]
• Transformers Generalize DeepSets and Can be Extended to Graphs & Hypergraphs (NeurIPS, 2021) [paper]
• Finding Bipartite Components in Hypergraphs (NeurIPS, 2021) [paper]
• Edge Representation Learning with Hypergraphs (NeurIPS, 2021) [paper]
• Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraphs (NeurIPS, 2020) [paper]
• HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs (NeurIPS, 2019) [paper]
• Inhomogeneous Hypergraph Clustering with Applications (NeurIPS, 2017) [paper]

International Conference on Learning Representations

• From Graphs to Hypergraphs: Hypergraph Projection and its Remediation (ICLR, 2024) [paper]
• Hypergraph Dynamic System (ICLR, 2024) [paper]
• HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs (ICLR, 2024) [paper]
• LightHGNN: Distilling Hypergraph Neural Networks into MLPs for 100x Faster Inference (ICLR, 2024) [paper]
• Equivariant Hypergraph Diffusion Neural Operators (ICLR, 2023) [paper]
• Ollivier-Ricci Curvature for Hypergraphs: A Unified Framework (ICLR, 2023) [paper]
• You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks (ICLR, 2022) [paper]
• Learning to Represent Action Values as a Hypergraph on the Action Vertices (ICLR, 2021) [paper]
• Hyper-SAGNN: a self-attention based graph neural network for hypergraphs (ICLR, 2020) [paper]

ACM Knowledge Discovery and Data Mining

• Classification of Edge-dependent Labels of Nodes in Hypergraphs (KDD, 2023) [paper]
• Mining of Real-world Hypergraphs: Patterns, Tools, and Generators (KDD, 2023) [paper]
• Learning Causal Effects on Hypergraphs (KDD Best Paper, 2022) [paper]
• Core-periphery Models for Hypergraphs (KDD, 2022) [paper]
• Self-Supervised Hypergraph Transformer for Recommender Systems (KDD, 2022) [paper]
• Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation (KDD, 2022) [paper]
• Generalizable Floorplanner through Corner Block List Representation and Hypergraph Embedding (KDD, 2022) [paper]
• H2MN: Graph Similarity Learning with Hierarchical Hypergraph Matching Networks (KDD, 2021) [paper]
• Structural Patterns and Generative Models of Real-world Hypergraphs (KDD, 2020) [paper]
• Minimizing Localized Ratio Cut Objectives in Hypergraphs (KDD, 2020) [paper]
• Parameterized Correlation Clustering in Hypergraphs and Bipartite Graphs (KDD, 2020) [paper]
• Hypergraph Clustering Based on PageRank (KDD, 2020) [paper]
• Dual Channel Hypergraph Collaborative Filtering (KDD, 2020) [paper]
• Hypergraph Convolutional Recurrent Neural Network (KDD, 2020) [paper]
• E-tail Product Return Prediction via Hypergraph-based Local Graph Cut (KDD, 2018) [paper]

International Conference on Research on Development in Information Retrieval

• Spatio-Temporal Hypergraph Learning for Next POI Recommendation (SIGIR, 2023) [paper]
• Multi-view Hypergraph Contrastive Policy Learning for Conversational Recommendation (SIGIR, 2023) [paper]
• Next Basket Recommendation with Intent-aware Hypergraph Adversarial Network (SIGIR, 2023) [paper]
• HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer (SIGIR, 2023) [paper]
• Co-clustering Interactions via Attentive Hypergraph Neural Network (SIGIR, 2022) [paper]
• Hypergraph Contrastive Collaborative Filtering (SIGIR, 2022) [paper]
• Enhancing Hypergraph Neural Networks with Intent Disentanglement for Session-based Recommendation (SIGIR, 2022) [paper]
• DH-HGCN: Dual Homogeneity Hypergraph Convolutional Network for Multiple Social Recommendations (SIGIR, 2022) [paper]
• Communication Efficient Distributed Hypergraph Clustering (SIGIR, 2021) [paper]
• Next-item Recommendation with Sequential Hypergraphs (SIGIR, 2020) [paper]

International World Wide Web Conferences

• HyConvE: A Novel Embedding Model for Knowledge Hypergraph Link Prediction with Convolutional Neural Networks (WWW, 2023) [paper]
• Intra and Inter Domain HyperGraph Convolutional Network for Cross-Domain Recommendation (WWW, 2023) [paper]
• Cut-matching Games for Generalized Hypergraph Ratio Cuts (WWW, 2023) [paper]
• Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation (WWW, 2023) [paper]
• IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search (WWW, 2022) [paper]
• MiDaS: Representative Sampling from Real-world Hypergraphs (WWW, 2022) [paper]
• Strongly Local Hypergraph Diffusions for Clustering and Semi-supervised Learning (WWW, 2021) [paper]
• Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks (WWW, 2021) [paper]
• Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation (WWW, 2021) [paper]
• How Do Hyperedges Overlap in Real-World Hypergraphs? - Patterns, Measures, and Generators (WWW, 2021) [paper]
• How Much and When Do We Need Higher-order Information in Hypergraphs? A Case Study on Hyperedge Prediction (WWW, 2020) [paper]
• Clustering in graphs and hypergraphs with categorical edge labels (WWW, 2020) [paper]
• Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach (WWW, 2019) [paper]
• Learning on Partial-Order Hypergraphs (WWW, 2018) [paper]

Artificial Intelligence and Statistics

• Hypergraph Simultaneous Generators (AISTATS, 2022) [paper]
• Statistical and computational thresholds for the planted k-densest sub-hypergraph problem (AISTATS, 2022) [paper]
• $𝐻𝑆^2$: Active learning over hypergraphs with pointwise and pairwise queries (AISTATS, 2019) [paper]
• Community Detection in Hypergraphs: Optimal Statistical Limit and Efficient Algorithms (AISTATS, 2018) [paper]
• Submodularity on Hypergraphs: From Sets to Sequences (AISTATS, 2018) [paper]

AAAI Conference on Artificial Intelligence

• Nested Named Entity Recognition as Building Local Hypergraphs (AAAI, 2023) [paper]
• Multi-Modal Knowledge Hypergraph for Diverse Image Retrieval (AAAI, 2023) [paper]
• Exploring Hypergraph of Earnings Call for Risk Prediction (AAAI, 2023) [paper]
• Hypergraph Modeling via Spectral Embedding Connection: Hypergraph Cut, Weighted Kernel k-Means, and Heat Kernel (AAAI, 2022) [paper]
• MS-HGAT: Memory-Enhanced Sequential Hypergraph Attention Network for Information Diffusion Prediction (AAAI, 2022) [paper]
• Adaptive Hypergraph Neural Network for Multi-Person Pose Estimation (AAAI, 2022) [paper]
• Compound Word Transformer: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphs (AAAI, 2021) [paper]
• Stock Selection via Spatiotemporal Hypergraph Attention Network: A Learning to Rank Approach (AAAI, 2021) [paper]
• Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation (AAAI, 2021) [paper]
• The Impact of Selfishness in Hypergraph Hedonic Games (AAAI, 2020) [paper]
• Hypergraph Label Propagation Network (AAAI, 2020) [paper]
• Wasserstein Soft Label Propagation on Hypergraphs: Algorithm and Generalization Error Bounds (AAAI, 2019) [paper]
• Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs (AAAI, 2019) [paper]
• Hypergraph Optimization for Multi-Structural Geometric Model Fitting (AAAI, 2019) [paper]
• Learning Non-Uniform Hypergraph for Multi-Object Tracking (AAAI, 2019) [paper]
• Hypergraph Neural Networks (AAAI, 2019) [paper]
• Hypergraph p-Laplacian: A Differential Geometry View (AAAI, 2018) [paper]
• Hypergraph Learning With Cost Interval Optimization (AAAI, 2018) [paper]

International Joint Conference on Artificial Intelligence

• Basket Representation Learning by Hypergraph Convolution on Repeated Items for Next-basket Recommendation (IJCAI, 2023) [paper]
• Towards Hierarchical Policy Learning for Conversational Recommendation with Hypergraph-based Reinforcement Learning (IJCAI, 2023) [paper]
• Totally Dynamic Hypergraph Neural Networks (IJCAI, 2023) [paper]
• Multi-view Contrastive Learning Hypergraph Neural Network for Drug-Microbe-Disease Association Prediction (IJCAI, 2023) [paper]
• Hypertron: Explicit Social-Temporal Hypergraph Framework for Multi-Agent Forecasting (IJCAI, 2022) [paper]
• Hypergraph Structure Learning for Hypergraph Neural Networks (IJCAI, 2022) [paper]
• Online ECG Emotion Recognition for Unknown Subjects via Hypergraph-Based Transfer Learning (IJCAI, 2022) [paper]
• Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning (IJCAI, 2021) [paper]
• UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks (IJCAI, 2021) [paper]
• Semi-Dynamic Hypergraph Neural Network for 3D Pose Estimation (IJCAI, 2020) [paper]
• Knowledge Hypergraphs: Prediction Beyond Binary Relations (IJCAI, 2020) [paper]
• Two-Phase Hypergraph Based Reasoning with Dynamic Relations for Multi-Hop KBQA (IJCAI, 2020) [paper]
• Dynamic Hypergraph Neural Networks (IJCAI, 2019) [paper]
• Hypergraph Induced Convolutional Manifold Networks (IJCAI, 2019) [paper]
• Dynamic Hypergraph Structure Learning (IJCAI, 2018) [paper]
• Synchronisation Games on Hypergraphs (IJCAI, 2017) [paper]
• Vertex-Weighted Hypergraph Learning for Multi-View Object Classification (IJCAI, 2017) [paper]
• Adaptive Hypergraph Learning for Unsupervised Feature Selection (IJCAI, 2017) [paper]
• Lossy Compression of Pattern Databases Using Acyclic Random Hypergraphs (IJCAI, 2017) [paper]

ACM International Conference on Information and Knowledge Management

• Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning (CIKM, 2023) [paper]
• Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph (CIKM, 2023) [paper]
• Seq-HyGAN: Sequence Classification via Hypergraph Attention Network (CIKM, 2023) [paper]
• Exploring Cohesive Subgraphs in Hypergraphs: The (k,g)-core Approach (CIKM, 2023) [paper]
• Semi-supervised Hypergraph Node Classification on Hypergraph Line Expansion (CIKM, 2022) [paper]
• Click-Through Rate Prediction with Multi-Modal Hypergraphs (CIKM, 2021) [paper]
• Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation (CIKM, 2021) [paper]
• Hyperbolic Hypergraphs for Sequential Recommendation (CIKM, 2021) [paper]
• HyperGraph Convolution Based Attributed HyperGraph Clustering (CIKM, 2021) [paper]
• Hypergraph Random Walks, Laplacians, and Clustering (CIKM, 2020) [paper]
• NHP: Neural Hypergraph Link Prediction (CIKM, 2020) [paper]
• Modeling Multi-way Relations with Hypergraph Embedding (CIKM, 2018) [paper]
• Computing Betweenness Centrality in B-hypergraphs (CIKM, 2017) [paper]
• Maintaining Densest Subsets Efficiently in Evolving Hypergraphs (CIKM, 2017) [paper]

International Conference on Data Mining

• Reciprocity in Directed Hypergraphs: Measures, Findings, and Generators (ICDM, 2022) [paper]
• THINK: Temporal Hypergraph Hyperbolic Network (ICDM, 2022) [paper]
• Hypergraph Convolutional Network for Group Recommendation (ICDM, 2021) [paper]
• THyMe+: Temporal Hypergraph Motifs and Fast Algorithms for Exact Counting (ICDM, 2021) [paper]
• Hypergraph Ego-networks and Their Temporal Evolution (ICDM, 2021) [paper]
• HyperTeNet: Hypergraph and Transformer-based Neural Network for Personalized List Continuation (ICDM, 2021) [paper]
• Spatiotemporal Hypergraph Convolution Network for Stock Movement Forecasting (ICDM, 2020) [paper]
• Evolution of Real-World Hypergraphs: Patterns and Models without Oracles (ICDM, 2020) [paper]

Journal Papers

IEEE Transactions on Pattern Analysis and Machine Intelligence

• Hypergraph Isomorphism Computation (TPAMI, 2024) [paper]
• HGNN+: General Hypergraph Neural Networks (TPAMI, 2023) [paper]
• Hypergraph Collaborative Network on Vertices and Hyperedges (TPAMI, 2023) [paper]
• Messages are Never Propagated Alone: Collaborative Hypergraph Neural Network for Time-Series Forecasting (TPAMI, 2023) [paper]
• Continual Image Deraining With Hypergraph Convolutional Networks (TPAMI, 2023) [paper]
• Generating Hypergraph-Based High-Order Representations of Whole-Slide Histopathological Images for Survival Prediction (TPAMI, 2023) [paper]
• Hypergraph-Based Multi-Modal Representation for Open-Set 3D Object Retrieval (TPAMI, 2023) [paper]
• Hypergraph Learning: Methods and Practices (TPAMI, 2022) [paper]
• Heterogeneous Hypergraph Variational Autoencoder for Link Prediction (TPAMI, 2022) [paper]
• Neural Graph Matching Network: Learning Lawler’s Quadratic Assignment Problem With Extension to Hypergraph and Multiple-Graph Matching (TPAMI, 2022) [paper]
• Learning on Hypergraphs With Sparsity (TPAMI, 2021) [paper]
• Searching for Representative Modes on Hypergraphs for Robust Geometric Model Fitting (TPAMI, 2019) [paper]
• Clustering with Hypergraphs: The Case for Large Hyperedges (TPAMI, 2017) [paper]
• An Efficient Multilinear Optimization Framework for Hypergraph Matching (TPAMI, 2017) [paper]
• Exploiting Hierarchical Dense Structures on Hypergraphs for Multi-Object Tracking (TPAMI, 2016) [paper]

IEEE Transactions on Knowledge and Data Engineering

• HyperISO: Efficiently Searching Subgraph Containment in Hypergraphs (TKDE, 2023) [paper]
• Adaptive Hypergraph Auto-Encoder for Relational Data Clustering (TKDE, 2023) [paper]
• Penalized Flow Hypergraph Local Clustering (TKDE, 2023) [paper]
• Hypergraph Representation for Detecting 3D Objects From Noisy Point Clouds (TKDE, 2023) [paper]
• Self-Supervised Hypergraph Representation Learning for Sociological Analysis (TKDE, 2023) [paper]
• Efficiently Counting Triangles for Hypergraph Streams by Reservoir-Based Sampling (TKDE, 2023) [paper]
• Hypergraph Partitioning With Embeddings (TKDE, 2022) [paper]
• Distributed Hypergraph Processing Using Intersection Graphs (TKDE, 2022) [paper]
• Data Representation by Joint Hypergraph Embedding and Sparse Coding (TKDE, 2022) [paper]
• HyperISO: Efficiently Searching Subgraph Containment in Hypergraphs (TKDE, 2022) [paper]
• Hypergraph Representation for Detecting 3D Objects from Noisy Point Clouds (TKDE, 2022) [paper]
• LBSN2Vec++: Heterogeneous Hypergraph Embedding for Location-Based Social Networks (TKDE, 2022) [paper]
• Adaptive Hypergraph Auto-Encoder for Relational Data Clustering (TKDE, 2021) [paper]
• Re-Revisiting Learning on Hypergraphs: Confidence Interval, Subgradient Method, and Extension to Multiclass (TKDE, 2020) [paper]
• HyperX: A Scalable Hypergraph Framework (TKDE, 2019) [paper]
• Dual Hypergraph Regularized PCA for Biclustering of Tumor Gene Expression Data (TKDE, 2019) [paper]
• Malevolent Activity Detection with Hypergraph-Based Models (TKDE, 2017) [paper]

IEEE Transactions on Image Processing

• Brain Network Analysis of Schizophrenia Patients Based on Hypergraph Signal Processing (TIP, 2023) [paper]
• Multimodal Remote Sensing Image Segmentation With Intuition-Inspired Hypergraph Modeling (TIP, 2023) [paper]
• An Efficient Hypergraph Approach to Robust Point Cloud Resampling (TIP, 2022) [paper]
• Big-Hypergraph Factorization Neural Network for Survival Prediction From Whole Slide Image (TIP, 2022) [paper]
• Hypergraph Spectral Analysis and Processing in 3D Point Cloud (TIP, 2021) [paper]
• Hypergraph Neural Network for Skeleton-Based Action Recognition (TIP, 2021) [paper]
• Multi-Scale Representation Learning on Hypergraph for 3D Shape Retrieval and Recognition (TIP, 2021) [paper]
• Correntropy-Induced Robust Low-Rank Hypergraph (TIP, 2019) [paper]
• Multimedia Retrieval Through Unsupervised Hypergraph-Based Manifold Ranking (TIP, 2019) [paper]
• Inductive Multi-Hypergraph Learning and Its Application on View-Based 3D Object Classification (TIP, 2018) [paper]
• Joint Hypergraph Learning for Tag-Based Image Retrieval (TIP, 2018) [paper]
• Elastic Net Hypergraph Learning for Image Clustering and Semi-Supervised Classification (TIP, 2017) [paper]

IEEE Transactions on Neural Networks and Learning Systems

• Central-Smoothing Hypergraph Neural Networks for Predicting Drug–Drug Interactions (TNNLS, 2023) [paper]
• Modeling High-Order Relationships: Brain-Inspired Hypergraph-Induced Multimodal-Multitask Framework for Semantic Comprehension (TNNLS, 2023) [paper]
• Music Recommendation via Hypergraph Embedding (TNNLS, 2023) [paper]
• Hypergraph Structural Information Aggregation Generative Adversarial Networks for Diagnosis and Pathogenetic Factors Identification of Alzheimer’s Disease With Imaging Genetic Data (TNNLS, 2022) [paper]
• Multi-Atlas Segmentation of Anatomical Brain Structures Using Hierarchical Hypergraph Learning (TNNLS, 2020) [paper]
• Hypergraph-Induced Convolutional Networks for Visual Classification (TNNLS, 2019) [paper]
• Learning to Map Social Network Users by Unified Manifold Alignment on Hypergraph (TNNLS, 2018) [paper]
• Person Re-identification by Multi-hypergraph Fusion (TNNLS, 2017) [paper]

IEEE Transactions on Cybernetics

• Prior-Guided Adversarial Learning With Hypergraph for Predicting Abnormal Connections in Alzheimer’s Disease (TCYB, 2024) [paper]
• Cost-Sensitive Hypergraph Learning With F-Measure Optimization (TCYB, 2023) [paper]
• Correntropy-Based Hypergraph Regularized NMF for Clustering and Feature Selection on Multi-Cancer Integrated Data (TCYB, 2021) [paper]
• Feature Learning Using Spatial-Spectral Hypergraph Discriminant Analysis for Hyperspectral Image (TCYB, 2019) [paper]
• Adaptive Discrete Hypergraph Matching (TCYB, 2018) [paper]
• Geometric Hypergraph Learning for Visual Tracking (TCYB, 2017) [paper]
• Unsupervised Topic Hypergraph Hashing for Efficient Mobile Image Retrieval (TCYB, 2017) [paper]

IEEE Transactions on Multimedia

• Automatic Hypergraph Generation for Enhancing Recommendation with Sparse Optimization (TMM, 2023) [paper]
• Adaptive Multi-Hypergraph Convolutional Networks for 3D Object Classification (TMM, 2023) [paper]
• A Universal Quaternion Hypergraph Network for Multimodal Video Question Answering (TMM, 2023) [paper]
• Object Cosegmentation in Noisy Videos With Multilevel Hypergraph (TMM, 2021) [paper]
• Cross-Modality Microblog Sentiment Prediction via Bi-Layer Multimodal Hypergraph Learning (TMM, 2019) [paper]
• Adaptive Hypergraph Embedded Semi-Supervised Multi-Label Image Annotation (TMM, 2019) [paper]
• Context-Aware Hypergraph Modeling for Re-identification and Summarization (TMM, 2016) [paper]

Hypergraph Theory

Conference Papers

IEEE Annual Symposium on Foundations of Computer Science

• Sparse random hypergraphs: Non-backtracking spectra and community detection (FOCS, 2022) [paper]
• Performance and limitations of the QAOA at constant levels on large sparse hypergraphs and spin glass models (FOCS, 2022) [paper]
• Spectral Hypergraph Sparsifiers of Nearly Linear Size (FOCS, 2021) [paper]
• Hypergraph k-cut for fixed k in deterministic polynomial time (FOCS, 2020) [paper]
• Near-linear Size Hypergraph Cut Sparsifiers (FOCS, 2020) [paper]
• Distributed Local Approximation Algorithms for Maximum Matching in Graphs and Hypergraphs (FOCS, 2019) [paper]
• New Notions and Constructions of Sparsification for Graphs and Hypergraphs (FOCS, 2019) [paper]
• The Average-Case Complexity of Counting Cliques in Erdős-Rényi Hypergraphs (FOCS, 2019) [paper]
• The Sketching Complexity of Graph and Hypergraph Counting (FOCS, 2018) [paper]
• A Characterization of Testable Hypergraph Properties (FOCS, 2017) [paper]
• Deterministic Distributed Edge-Coloring via Hypergraph Maximal Matching (FOCS, 2017) [paper]

ACM-SIAM Symposium on Discrete Algorithms

• Conflict-free hypergraph matchings (SODA, 2023) [paper]
• Zigzagging through acyclic orientations of chordal graphs and hypergraphs (SODA, 2023)
• A simple and sharper proof of the hypergraph Moore bound (SODA, 2023) [paper]
• Distributed Maximal Matching and Maximal Independent Set on Hypergraphs (SODA, 2023) [paper]
• On the complexity of binary polynomial optimization over acyclic hypergraphs (SODA, 2022) [paper]
• Approximate Hypergraph Vertex Cover and generalized Tuza's conjecture (SODA, 2022) [paper]
• Deterministic enumeration of all minimum k-cut-sets in hypergraphs for fixed k (SODA, 2022) [paper]
• Min-max Partitioning of Hypergraphs and Symmetric Submodular Functions (SODA, 2021) [paper]
• Non-linear Hamilton cycles in linear quasi-random hypergraphs (SODA, 2021) [paper]
• Finding Perfect Matchings in Dense Hypergraphs (SODA, 2020) [paper]
• A Tale of Santa Claus, Hypergraphs and Matroids (SODA, 2020) [paper]
• Factors and loose Hamilton cycles in sparse pseudo-random hypergraphs (SODA, 2020) [paper]
• Spectral Sparsification of Hypergraphs (SODA, 2019) [paper]
• Minimum Cut and Minimum k-Cut in Hypergraphs via Branching Contractions (SODA, 2019) [paper]
• Derandomized concentration bounds for polynomials, and hypergraph maximal independent set (SODA, 2018) [paper]
• Hypergraph k-Cut in Randomized Polynomial Time (SODA, 2018) [paper]
• Computing minimum cuts in hypergraphs (SODA, 2017) [paper]
• Random Contractions and Sampling for Hypergraph and Hedge Connectivity (SODA, 2017) [paper]
• Tight Algorithms for Vertex Cover with Hard Capacities on Multigraphs and Hypergraphs (SODA, 2017) [paper]
• The complexity of approximately counting in 2-spin systems on k-uniform bounded-degree hypergraphs (SODA, 2016) [paper]
• An Algorithmic Hypergraph Regularity Lemma (SODA, 2016) [paper]
• Finding Perfect Matchings in Bipartite Hypergraphs (SODA, 2016) [paper]

ACM Symposium on Theory of Computing

• Chaining, Group Leverage Score Overestimates, and Fast Spectral Hypergraph Sparsification (STOC, 2023) [paper]
• Cheeger Inequalities for Directed Graphs and Hypergraphs using Reweighted Eigenvalues (STOC, 2023) [paper]
• Algorithmic Applications of Hypergraph and Partition Containers (STOC, 2023) [paper]
• Spectral Hypergraph Sparsification via Chaining (STOC, 2023) [paper]
• Towards tight bounds for spectral sparsification of hypergraphs (STOC, 2021) [paper]
• Extractors for adversarial sources via extremal hypergraphs (STOC, 2020) [paper]
• Counting hypergraph colourings in the local lemma regime (STOC, 2018) [paper]

Conference on Learning Theory

• Community Detection in the Hypergraph SBM: Optimal Recovery Given the Similarity Matrix (COLT, 2023) [paper]
• Weak Recovery Threshold for the Hypergraph Stochastic Block Model (COLT, 2023) [paper]
• Learning Low Degree Hypergraphs (COLT, 2022) [paper]
• Open Problem: Average-Case Hardness of Hypergraphic Planted Clique Detection (COLT, 2020) [paper]

European Symposium on Algorithms

• Counting Simplices in Hypergraph Streams (ESA, 2022) [paper]
• Fully Dynamic Set Cover via Hypergraph Maximal Matching: An Optimal Approximation Through a Local Approach (ESA, 2021) [paper]
• The Minimization of Random Hypergraphs (ESA, 2020) [paper]
• Distributed Algorithms for Matching in Hypergraphs (ESA, 2020) [paper]
• Dense Peelable Random Uniform Hypergraphs (ESA, 2019) [paper]
• Evaluation of a Flow-Based Hypergraph Bipartitioning Algorithm (ESA, 2019) [paper]
• Clustering in Hypergraphs to Minimize Average Edge Service Time (ESA, 2017) [paper]

Journal Papers

Journal of the ACM

• Spectral Properties of Hypergraph Laplacian and Approximation Algorithms (JACM, 2018) [paper]

Journal of Machine Learning Research

• Augmented Sparsifiers for Generalized Hypergraph Cuts (JMLR, 2023) [paper]
• Knowledge Hypergraph Embedding Meets Relational Algebra (JMLR, 2023) [paper]
• Nonparametric modeling of higher-order interactions via hypergraphons (JMLR, 2021) [paper]
• Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques (JMLR, 2017) [paper]

IEEE Transactions on Information Theory

• Pliable Index Coding via Conflict-Free Colorings of Hypergraphs (TIT, 2024) [paper]
• Strong Consistency of Spectral Clustering for the Sparse Degree-Corrected Hypergraph Stochastic Block Model (TIT, 2023) [paper]
• Exact Recovery in the General Hypergraph Stochastic Block Model (TIT, 2023) [paper]
• Limitations on Transversal Gates for Hypergraph Product Codes (TIT, 2022) [paper]
• ReShape: A Decoder for Hypergraph Product Codes (TIT, 2022) [paper]
• On Some Distributed Scheduling Algorithms for Wireless Networks With Hypergraph Interference Models (TIT, 2021) [paper]
• Motif and Hypergraph Correlation Clustering (TIT, 2020) [paper]
• Community Recovery in Hypergraphs (TIT, 2019) [paper]
• On the Minimax Misclassification Ratio of Hypergraph Community Detection (TIT, 2019) [paper]
• Centralized Coded Caching Schemes: A Hypergraph Theoretical Approach (TIT, 2018) [paper]
• New Lower Bounds for Secure Codes and Related Hash Families: A Hypergraph Theoretical Approach (TIT, 2017) [paper]

Hypergraph Tutorial

• Mining of Real-world Hypergraphs: Patterns, Tools, and Generators (CIKM, 2022) [website]
• Multimodal deep learning on hypergraphs (University of Amsterdam, 2022) [PhD Thesis]

Hypergraph Dataset

Classification

	Cora	Citeseer	Pubmed	Cora-CA	DBLP-CA	Zoo	20News
#Vertex	2708	3312	19717	2708	41302	101	16242
#Hyperedge	1579	1079	7963	1072	22363	43	100
#Feature	1433	3703	500	1433	1425	16	100
#Class	7	6	3	7	6	7	4
Max Hyperedge Size	5	26	171	43	202	93	2241

	Mushroom	NTU2012	ModelNet40	Yelp	House	Walmart
#Vertex	8124	2012	12311	50758	1290	88860
#Hyperedge	298	2012	12311	679302	341	69906
#Feature	22	100	100	1862	100	100
#Class	2	67	40	9	2	11
Max Hyperedge Size	1808	5	5	2838	81	25

Hypergraph node classification dataset is available at https://github.com/jianhao2016/AllSet

Clustering

Hypergraph clustering dataset is available at https://sites.google.com/view/panli-purdue/datasets

Partitioning

Hypergraph partitioning dataset is available at https://kahypar.org/

Hypergraph Tool

PyTorch Geometric: https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#convolutional-layers (Hypergraph Convolution Network)

DeepHypergraph: https://github.com/iMoonLab/DeepHypergraph (Hypergraph Neural Networks)

OpenHGNN: https://github.com/BUPT-GAMMA/OpenHGNN (Heterogeneous Graph Neural Network)

HyperNetX: https://github.com/pnnl/HyperNetX (Community Detection, Clustering, Generation, Visualization)

KaHyPar: https://github.com/kahypar/kahypar (Hypergraph Partitioning)

HAT: https://github.com/Jpickard1/Hypergraph-Analysis-Toolbox (Hypergraph Analysis)

Hypergraph: https://github.com/yamafaktory/hypergraph (Data Structure)

XGI: https://github.com/xgi-org/xgi (Hypergraph Group Interaction)

Hypergraph Task

Hypergraph Embedding: https://paperswithcode.com/task/hypergraph-embedding

Hypergraph Matching: https://paperswithcode.com/task/hypergraph-matching

Hypergraph Representations: https://paperswithcode.com/task/hypergraph-representations

Hypergraph Partitioning: https://paperswithcode.com/task/hypergraph-partitioning

Hypergraph Expansion

awesome-hypergraph-network's Issues

Adding the code repo links

Hi Zhicheng,

Thanks for the great summary! This is Shuai and I also work on hypergraph projects. I can add the code repo links to these papers, if you think it would be helpful. Let me know if you are interested!

Best,
Shuai