{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/graph-attention/papers/8","list_of":"/task/graph-attention","task":"Graph Attention","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":8,"pages_in_order":11,"rows_per_page":100,"rows":[701,800],"of":1088,"counts":{"archive_papers_tagged":1088,"with_a_code_link":413,"where_syntology_ran_a_sample":60,"not_listed_spam_title":0,"listed":1088,"listed_where_code_ran":60,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":52,"every_run_a_failure_of_syntologys_instrument":8,"listed_with_a_run_with_no_instrument_failure":52,"listed_every_run_a_failure_of_syntologys_instrument":8,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/graph-attention","prev":"/task/graph-attention/papers/7","next":"/task/graph-attention/papers/9","papers":[{"url":null,"slug":"higher-order-graph-attention-network-for","title":"Higher-order Graph Attention Network for Stock Selection with Joint Analysis","date":"2023-06-27","arxiv_id":"2306.15526","repositories_listed":0,"syntology":null},{"url":null,"slug":"input-sensitive-dense-sparse-primitive","title":"SENSEi: Input-Sensitive Compilation for Accelerating GNNs","date":"2023-06-27","arxiv_id":"2306.15155","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-language-speech-emotion-recognition","title":"Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers","date":"2023-06-23","arxiv_id":"2306.13804","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-driven-graph-attention-for-hierarchical","title":"Task-Driven Graph Attention for Hierarchical Relational Object Navigation","date":"2023-06-23","arxiv_id":"2306.13760","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-of-annual-snow-accumulation-using","title":"Prediction of Annual Snow Accumulation Using a Recurrent Graph Convolutional Approach","date":"2023-06-22","arxiv_id":"2306.13181","repositories_listed":0,"syntology":null},{"url":null,"slug":"transforming-graphs-for-enhanced-attribute","title":"Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method","date":"2023-06-20","arxiv_id":"2306.11307","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-and-position-aware-learning-in","title":"Explainable and Position-Aware Learning in Digital Pathology","date":"2023-06-14","arxiv_id":"2306.08198","repositories_listed":0,"syntology":null},{"url":null,"slug":"inferring-dynamic-regulatory-interaction","title":"Inferring dynamic regulatory interaction graphs from time series data with perturbations","date":"2023-06-13","arxiv_id":"2306.07803","repositories_listed":0,"syntology":null},{"url":null,"slug":"nfts-to-mars-multi-attention-recommender","title":"NFTs to MARS: Multi-Attention Recommender System for NFTs","date":"2023-06-13","arxiv_id":"2306.10053","repositories_listed":0,"syntology":null},{"url":null,"slug":"coupled-attention-networks-for-multivariate","title":"Coupled Attention Networks for Multivariate Time Series Anomaly Detection","date":"2023-06-12","arxiv_id":"2306.07114","repositories_listed":0,"syntology":null},{"url":null,"slug":"origin-destination-network-generation-via","title":"Origin-Destination Network Generation via Gravity-Guided GAN","date":"2023-06-06","arxiv_id":"2306.03390","repositories_listed":0,"syntology":null},{"url":null,"slug":"gat-gan-a-graph-attention-based-time-series","title":"GAT-GAN : A Graph-Attention-based Time-Series Generative Adversarial Network","date":"2023-06-03","arxiv_id":"2306.01999","repositories_listed":0,"syntology":null},{"url":null,"slug":"4dsr-gcn-4d-video-point-cloud-upsampling","title":"4DSR-GCN: 4D Video Point Cloud Upsampling using Graph Convolutional Networks","date":"2023-06-01","arxiv_id":"2306.01081","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-tradeoff-for-heterogeneous-graph","title":"Semantic tradeoff for heterogeneous graph embedding","date":"2023-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tofg-a-unified-and-fine-grained-environment","title":"TOFG: A Unified and Fine-Grained Environment Representation in Autonomous Driving","date":"2023-05-31","arxiv_id":"2305.20068","repositories_listed":0,"syntology":null},{"url":null,"slug":"fern-leveraging-graph-attention-networks-for","title":"FERN: Leveraging Graph Attention Networks for Failure Evaluation and Robust Network Design","date":"2023-05-30","arxiv_id":"2305.19153","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-network-based-on-sparse","title":"Convolutional neural network based on sparse graph attention mechanism for MRI super-resolution","date":"2023-05-29","arxiv_id":"2305.17898","repositories_listed":0,"syntology":null},{"url":null,"slug":"demystifying-oversmoothing-in-attention-based","title":"Demystifying Oversmoothing in Attention-Based Graph Neural Networks","date":"2023-05-25","arxiv_id":"2305.16102","repositories_listed":0,"syntology":null},{"url":null,"slug":"gatology-for-linguistics-what-syntactic","title":"GATology for Linguistics: What Syntactic Dependencies It Knows","date":"2023-05-22","arxiv_id":"2305.13403","repositories_listed":0,"syntology":null},{"url":null,"slug":"syntactic-knowledge-via-graph-attention-with","title":"Syntactic Knowledge via Graph Attention with BERT in Machine Translation","date":"2023-05-22","arxiv_id":"2305.13413","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-short-term-wind-speed-forecasting","title":"Enhancing Short-Term Wind Speed Forecasting using Graph Attention and Frequency-Enhanced Mechanisms","date":"2023-05-19","arxiv_id":"2305.11526","repositories_listed":0,"syntology":null},{"url":null,"slug":"st-gin-an-uncertainty-quantification-approach","title":"ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks","date":"2023-05-10","arxiv_id":"2305.06480","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-based-feature-fusion-for-multi","title":"Attention Based Feature Fusion For Multi-Agent Collaborative Perception","date":"2023-05-03","arxiv_id":"2305.02061","repositories_listed":0,"syntology":null},{"url":null,"slug":"gradient-derivation-for-learnable-parameters","title":"Gradient Derivation for Learnable Parameters in Graph Attention Networks","date":"2023-04-21","arxiv_id":"2304.10939","repositories_listed":0,"syntology":null},{"url":null,"slug":"tc-gat-graph-attention-network-for-temporal","title":"TC-GAT: Graph Attention Network for Temporal Causality Discovery","date":"2023-04-21","arxiv_id":"2304.10706","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-graph-structure-information-for","title":"Investigating Graph Structure Information for Entity Alignment with Dangling Cases","date":"2023-04-10","arxiv_id":"2304.04718","repositories_listed":0,"syntology":null},{"url":null,"slug":"bs-gat-behavior-similarity-based-graph","title":"BS-GAT Behavior Similarity Based Graph Attention Network for Network Intrusion Detection","date":"2023-04-07","arxiv_id":"2304.07226","repositories_listed":0,"syntology":null},{"url":null,"slug":"lmexplainer-a-knowledge-enhanced-explainer","title":"LMExplainer: Grounding Knowledge and Explaining Language Models","date":"2023-03-29","arxiv_id":"2303.16537","repositories_listed":0,"syntology":null},{"url":"/paper/who-you-play-affects-how-you-play-predicting","slug":"who-you-play-affects-how-you-play-predicting","title":"Who You Play Affects How You Play: Predicting Sports Performance Using Graph Attention Networks With Temporal Convolution","date":"2023-03-29","arxiv_id":"2303.16741","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-enhanced-graph-neural-networks","title":"Knowledge Enhanced Graph Neural Networks for Graph Completion","date":"2023-03-27","arxiv_id":"2303.15487","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairgat-fairness-aware-graph-attention","title":"FairGAT: Fairness-aware Graph Attention Networks","date":"2023-03-26","arxiv_id":"2303.14591","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatio-temporal-driven-attention-graph-neural","title":"Spatio-Temporal driven Attention Graph Neural Network with Block Adjacency matrix (STAG-NN-BA)","date":"2023-03-25","arxiv_id":"2303.14322","repositories_listed":0,"syntology":null},{"url":null,"slug":"stgic-a-graph-and-image-convolution-based","title":"STGIC: a graph and image convolution-based method for spatial transcriptomic clustering","date":"2023-03-19","arxiv_id":"2303.10657","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-graph-feature-fusion-technique-for","title":"The Graph feature fusion technique for speaker recognition based on wav2vec2.0 framework","date":"2023-03-19","arxiv_id":"2303.10556","repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-efficient-cellular-connected-uav-swarm","title":"Energy-Efficient Cellular-Connected UAV Swarm Control Optimization","date":"2023-03-18","arxiv_id":"2303.10398","repositories_listed":0,"syntology":null},{"url":"/paper/luke-graph-a-transformer-based-approach-with","slug":"luke-graph-a-transformer-based-approach-with","title":"LUKE-Graph: A Transformer-based Approach with Gated Relational Graph Attention for Cloze-style Reading Comprehension","date":"2023-03-12","arxiv_id":"2303.06675","repositories_listed":0,"syntology":null},{"url":null,"slug":"dedgat-dual-embedding-of-directed-graph","title":"DEDGAT: Dual Embedding of Directed Graph Attention Networks for Detecting Financial Risk","date":"2023-03-06","arxiv_id":"2303.03933","repositories_listed":0,"syntology":null},{"url":"/paper/diffusing-graph-attention","slug":"diffusing-graph-attention","title":"Diffusing Graph Attention","date":"2023-03-01","arxiv_id":"2303.00613","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-spatio-temporal-correlation-based","title":"Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting","date":"2023-02-17","arxiv_id":"2302.08658","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-attention-multi-agent-fleet-autonomy","title":"Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility","date":"2023-02-14","arxiv_id":"2302.07337","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-graph-neural-networks-for-graph","title":"A Comprehensive Survey on Graph Summarization with Graph Neural Networks","date":"2023-02-13","arxiv_id":"2302.06114","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-class-brain-tumor-segmentation-using","title":"Multi-class Brain Tumor Segmentation using Graph Attention Network","date":"2023-02-11","arxiv_id":"2302.05598","repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-the-privacy-leakage-via-graph","title":"Measuring the Privacy Leakage via Graph Reconstruction Attacks on Simplicial Neural Networks (Student Abstract)","date":"2023-02-08","arxiv_id":"2302.04373","repositories_listed":0,"syntology":null},{"url":null,"slug":"heterophily-aware-graph-attention-network","title":"Heterophily-Aware Graph Attention Network","date":"2023-02-07","arxiv_id":"2302.03228","repositories_listed":0,"syntology":null},{"url":null,"slug":"jpeg-steganalysis-based-on-steganographic","title":"JPEG Steganalysis Based on Steganographic Feature Enhancement and Graph Attention Learning","date":"2023-02-05","arxiv_id":"2302.02276","repositories_listed":0,"syntology":null},{"url":null,"slug":"searching-large-neighborhoods-for-integer","title":"Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning","date":"2023-02-03","arxiv_id":"2302.01578","repositories_listed":0,"syntology":null},{"url":null,"slug":"open-set-multi-source-multi-target-domain","title":"Open-Set Multi-Source Multi-Target Domain Adaptation","date":"2023-02-02","arxiv_id":"2302.00995","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-attention-with-hierarchies-for-multi","title":"Graph Attention with Hierarchies for Multi-hop Question Answering","date":"2023-01-27","arxiv_id":"2301.11792","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-depth-graph-attention-networks","title":"Adaptive Depth Graph Attention Networks","date":"2023-01-16","arxiv_id":"2301.06265","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequential-graph-attention-learning-for","title":"Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract)","date":"2023-01-15","arxiv_id":"2301.10153","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-aware-hyperbolic-graph-attention-network","title":"Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation","date":"2023-01-10","arxiv_id":"2301.03780","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-over-smoothing-in-graph-neural","title":"Reducing Over-smoothing in Graph Neural Networks Using Relational Embeddings","date":"2023-01-07","arxiv_id":"2301.02924","repositories_listed":0,"syntology":null},{"url":null,"slug":"guap-graph-universal-attack-through","title":"GUAP: Graph Universal Attack Through Adversarial Patching","date":"2023-01-04","arxiv_id":"2301.01731","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-approach-to-polymerization","title":"Machine Learning Approach to Polymerization Reaction Engineering: Determining Monomers Reactivity Ratios","date":"2023-01-03","arxiv_id":"2301.01231","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-3d-shape-classification-via-non-local","title":"Robust 3D Shape Classification via Non-Local Graph Attention Network","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-traffic-knowledge-graph-generation","title":"Visual Traffic Knowledge Graph Generation from Scene Images","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-strategies-for-cooperative-multi","title":"Hierarchical Strategies for Cooperative Multi-Agent Reinforcement Learning","date":"2022-12-14","arxiv_id":"2212.07397","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-learning-for-anomaly-analytics","title":"Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges","date":"2022-12-11","arxiv_id":"2212.05532","repositories_listed":0,"syntology":null},{"url":null,"slug":"productgraphsleepnet-sleep-staging-using","title":"ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregation","date":"2022-12-09","arxiv_id":"2212.04881","repositories_listed":0,"syntology":null},{"url":null,"slug":"face-forgery-detection-based-on-facial-region","title":"Face Forgery Detection Based on Facial Region Displacement Trajectory Series","date":"2022-12-07","arxiv_id":"2212.03678","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-edge-prediction-in-temporally","title":"Multi-Task Edge Prediction in Temporally-Dynamic Video Graphs","date":"2022-12-06","arxiv_id":"2212.02875","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-good-trajectories-in-offline","title":"Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning","date":"2022-11-28","arxiv_id":"2211.15612","repositories_listed":0,"syntology":null},{"url":null,"slug":"si-gat-a-method-based-on-improved-graph","title":"SI-GAT: A method based on improved Graph Attention Network for sonar image classification","date":"2022-11-28","arxiv_id":"2211.15133","repositories_listed":0,"syntology":null},{"url":null,"slug":"pu-gnn-chargeback-fraud-detection-in-p2e","title":"PU GNN: Chargeback Fraud Detection in P2E MMORPGs via Graph Attention Networks with Imbalanced PU Labels","date":"2022-11-16","arxiv_id":"2211.08604","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-decomposition-improves-learning-of","title":"Semantic Decomposition Improves Learning of Large Language Models on EHR Data","date":"2022-11-14","arxiv_id":"2212.06040","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-traffic-state-forecasting-using","title":"Efficient Traffic State Forecasting using Spatio-Temporal Network Dependencies: A Sparse Graph Neural Network Approach","date":"2022-11-06","arxiv_id":"2211.03033","repositories_listed":0,"syntology":null},{"url":null,"slug":"fradulent-user-detection-via-behavior","title":"Fraudulent User Detection Via Behavior Information Aggregation Network (BIAN) On Large-Scale Financial Social Network","date":"2022-11-04","arxiv_id":"2211.06315","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-explainability-methods-for-graph","title":"Exploring Explainability Methods for Graph Neural Networks","date":"2022-11-03","arxiv_id":"2211.01770","repositories_listed":0,"syntology":null},{"url":null,"slug":"factor-investing-with-a-deep-multi-factor","title":"Factor Investing with a Deep Multi-Factor Model","date":"2022-10-22","arxiv_id":"2210.12462","repositories_listed":0,"syntology":null},{"url":null,"slug":"causally-guided-regularization-of-graph","title":"Causally-guided Regularization of Graph Attention Improves Generalizability","date":"2022-10-20","arxiv_id":"2210.10946","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-attention-networks-unveil-determinants","title":"Graph Attention Networks Unveil Determinants of Intra- and Inter-city Health Disparity","date":"2022-10-18","arxiv_id":"2210.10142","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-classification-thresholds-for-graph","title":"On Classification Thresholds for Graph Attention with Edge Features","date":"2022-10-18","arxiv_id":"2210.10014","repositories_listed":0,"syntology":null},{"url":null,"slug":"not-all-neighbors-are-worth-attending-to","title":"Not All Neighbors Are Worth Attending to: Graph Selective Attention Networks for Semi-supervised Learning","date":"2022-10-14","arxiv_id":"2210.07715","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-semantic-representation-learning","title":"Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information","date":"2022-10-07","arxiv_id":"2210.03292","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-machine-learning-assisted","title":"Large-scale machine-learning-assisted exploration of the whole materials space","date":"2022-10-02","arxiv_id":"2210.00579","repositories_listed":0,"syntology":null},{"url":null,"slug":"ji-yu-guan-xi-tu-zhu-yi-li-wang-luo-he-kuan","title":"基于关系图注意力网络和宽度学习的负面情绪识别方法(Negative Emotion Recognition Method Based on Rational Graph Attention Network and Broad Learning)","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-perspective-scientific-document","title":"Multi Perspective Scientific Document Summarization With Graph Attention Networks (GATS)","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"stgin-a-spatial-temporal-graph-informer","title":"STGIN: A Spatial Temporal Graph-Informer Network for Long Sequence Traffic Speed Forecasting","date":"2022-10-01","arxiv_id":"2210.01799","repositories_listed":0,"syntology":null},{"url":null,"slug":"topology-imbalance-and-relation","title":"Topology Imbalance and Relation Inauthenticity Aware Hierarchical Graph Attention Networks for Fake News Detection","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-graph-based-recommender-system-with","title":"Efficient Graph based Recommender System with Weighted Averaging of Messages","date":"2022-09-30","arxiv_id":"2209.15238","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-attention-network-for-camera","title":"Graph Attention Network for Camera Relocalization on Dynamic Scenes","date":"2022-09-29","arxiv_id":"2209.15056","repositories_listed":0,"syntology":null},{"url":null,"slug":"pearnet-a-pearson-correlation-based-graph","title":"PearNet: A Pearson Correlation-based Graph Attention Network for Sleep Stage Recognition","date":"2022-09-26","arxiv_id":"2209.13645","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-attentive-belief-propagation-integrating","title":"Deep Attentive Belief Propagation: Integrating Reasoning and Learning for Solving Constraint Optimization Problems","date":"2022-09-24","arxiv_id":"2209.12000","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-query-rewriting-for-improving-1","title":"Context-Aware Query Rewriting for Improving Users' Search Experience on E-commerce Websites","date":"2022-09-15","arxiv_id":"2209.07584","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-modeling-of-network-flows","title":"Graph Neural Modeling of Network Flows","date":"2022-09-12","arxiv_id":"2209.05208","repositories_listed":0,"syntology":null},{"url":null,"slug":"landmark-enhanced-multimodal-graph-learning","title":"Multimodal Graph Learning for Deepfake Detection","date":"2022-09-12","arxiv_id":"2209.05419","repositories_listed":0,"syntology":null},{"url":null,"slug":"spotting-virus-from-satellites-modeling-the","title":"Spotting Virus from Satellites: Modeling the Circulation of West Nile Virus Through Graph Neural Networks","date":"2022-09-07","arxiv_id":"2209.05251","repositories_listed":0,"syntology":null},{"url":null,"slug":"spiking-gats-learning-graph-attentions-via","title":"Spiking GATs: Learning Graph Attentions via Spiking Neural Network","date":"2022-09-05","arxiv_id":"2209.13539","repositories_listed":0,"syntology":null},{"url":null,"slug":"lifelong-learning-for-neural-powered-mixed","title":"Lifelong Learning for Neural powered Mixed Integer Programming","date":"2022-08-24","arxiv_id":"2208.12226","repositories_listed":0,"syntology":null},{"url":null,"slug":"memonav-selecting-informative-memories-for","title":"MemoNav: Selecting Informative Memories for Visual Navigation","date":"2022-08-20","arxiv_id":"2208.09610","repositories_listed":0,"syntology":null},{"url":null,"slug":"locally-temporal-spatial-pattern-learning","title":"Locally temporal-spatial pattern learning with graph attention mechanism for EEG-based emotion recognition","date":"2022-08-19","arxiv_id":"2208.11087","repositories_listed":0,"syntology":null},{"url":null,"slug":"memory-efficient-temporal-visual-graph-model","title":"Memory Efficient Temporal & Visual Graph Model for Unsupervised Video Domain Adaptation","date":"2022-08-13","arxiv_id":"2208.06554","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-point-processes-using-recurrent","title":"Learning Point Processes using Recurrent Graph Network","date":"2022-08-11","arxiv_id":"2208.05736","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-the-spatio-temporal-dynamics-of","title":"Analysis of the Spatio-temporal Dynamics of COVID-19 in Massachusetts via Spectral Graph Wavelet Theory","date":"2022-07-28","arxiv_id":"2208.01749","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-graph-neural-networks-for-program","title":"Using Graph Neural Networks for Program Termination","date":"2022-07-28","arxiv_id":"2207.14648","repositories_listed":0,"syntology":null},{"url":null,"slug":"distance-geometric-graph-attention-network-dg","title":"Distance-Geometric Graph Attention Network (DG-GAT) for 3D Molecular Geometry","date":"2022-07-16","arxiv_id":"2207.08023","repositories_listed":0,"syntology":null},{"url":null,"slug":"ddi-prediction-via-heterogeneous-graph","title":"DDI Prediction via Heterogeneous Graph Attention Networks","date":"2022-07-12","arxiv_id":"2207.05672","repositories_listed":0,"syntology":null},{"url":null,"slug":"fd-gatdr-a-federated-decentralized-learning","title":"FD-GATDR: A Federated-Decentralized-Learning Graph Attention Network for Doctor Recommendation Using EHR","date":"2022-07-11","arxiv_id":"2207.05750","repositories_listed":0,"syntology":null},{"url":null,"slug":"attributed-abnormality-graph-embedding-for","title":"Attributed Abnormality Graph Embedding for Clinically Accurate X-Ray Report Generation","date":"2022-07-04","arxiv_id":"2207.01208","repositories_listed":0,"syntology":null},{"url":null,"slug":"cybersecurity-entity-alignment-via-masked","title":"Cybersecurity Entity Alignment via Masked Graph Attention Networks","date":"2022-07-04","arxiv_id":"2207.01434","repositories_listed":0,"syntology":null}],"record_sha256":"9af8002c8e3a7f914893e34da518dbb77c935c48acefb3fe32f343daaa600634","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}