{"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-neural-network/papers/34","list_of":"/task/graph-neural-network","task":"Graph Neural Network","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":34,"pages_in_order":36,"rows_per_page":100,"rows":[3301,3400],"of":3598,"counts":{"archive_papers_tagged":3598,"with_a_code_link":1516,"where_syntology_ran_a_sample":350,"not_listed_spam_title":0,"listed":3598,"listed_where_code_ran":350,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":302,"every_run_a_failure_of_syntologys_instrument":48,"listed_with_a_run_with_no_instrument_failure":302,"listed_every_run_a_failure_of_syntologys_instrument":48,"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-neural-network","prev":"/task/graph-neural-network/papers/33","next":"/task/graph-neural-network/papers/35","papers":[{"url":null,"slug":"discovering-dialog-structure-graph-for-open","title":"Discovering Dialog Structure Graph for Open-Domain Dialog Generation","date":"2020-12-31","arxiv_id":"2012.15543","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-air-quality-and-weather-prediction","title":"Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks","date":"2020-12-30","arxiv_id":"2012.15037","repositories_listed":0,"syntology":null},{"url":null,"slug":"signed-graph-diffusion-network-1","title":"Signed Graph Diffusion Network","date":"2020-12-28","arxiv_id":"2012.14191","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-inductive-learner-for-graph","title":"Bayesian Graph Neural Network for Fast identification of critical nodes in Uncertain Complex Networks","date":"2020-12-26","arxiv_id":"2012.15733","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-network-based-approximation-of","title":"Scalable Graph Neural Network-based framework for identifying critical nodes and links in Complex Networks","date":"2020-12-26","arxiv_id":"2012.15725","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hierarchical-reasoning-graph-neural-network","title":"A Hierarchical Reasoning Graph Neural Network for The Automatic Scoring of Answer Transcriptions in Video Job Interviews","date":"2020-12-22","arxiv_id":"2012.11960","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-multi-attribute-graph-representation","title":"Deep Multi-attribute Graph Representation Learning on Protein Structures","date":"2020-12-22","arxiv_id":"2012.11762","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-graph-reasoning-network-for-multi-turn","title":"A Graph Reasoning Network for Multi-turn Response Selection via Customized Pre-training","date":"2020-12-21","arxiv_id":"2012.11099","repositories_listed":0,"syntology":null},{"url":null,"slug":"hop-hop-relation-aware-graph-neural-networks","title":"Hop-Hop Relation-aware Graph Neural Networks","date":"2020-12-21","arxiv_id":"2012.11147","repositories_listed":0,"syntology":null},{"url":null,"slug":"learn-molecular-representations-from-large","title":"Learn molecular representations from large-scale unlabeled molecules for drug discovery","date":"2020-12-21","arxiv_id":"2012.11175","repositories_listed":0,"syntology":null},{"url":null,"slug":"suspicious-massive-registration-detection-via","title":"Suspicious Massive Registration Detection via Dynamic Heterogeneous Graph Neural Networks","date":"2020-12-20","arxiv_id":"2012.10831","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-meta-path-contexts-for","title":"Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks","date":"2020-12-18","arxiv_id":"2012.10024","repositories_listed":0,"syntology":null},{"url":"/paper/pc-rgnn-point-cloud-completion-and-graph","slug":"pc-rgnn-point-cloud-completion-and-graph","title":"PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection","date":"2020-12-18","arxiv_id":"2012.10412","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-graph-neural-network-approach-for","title":"A Hybrid Graph Neural Network Approach for Detecting PHP Vulnerabilities","date":"2020-12-16","arxiv_id":"2012.08835","repositories_listed":0,"syntology":null},{"url":null,"slug":"molecular-graph-generation-with-graph-neural","title":"Molecular graph generation with Graph Neural Networks","date":"2020-12-14","arxiv_id":"2012.07397","repositories_listed":0,"syntology":null},{"url":null,"slug":"gnn-xml-graph-neural-networks-for-extreme","title":"GNN-XML: Graph Neural Networks for Extreme Multi-label Text Classification","date":"2020-12-10","arxiv_id":"2012.05860","repositories_listed":0,"syntology":null},{"url":"/paper/spatiotemporal-graph-neural-network-based","slug":"spatiotemporal-graph-neural-network-based","title":"Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation","date":"2020-12-10","arxiv_id":"2012.05499","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-video-instance-segmentation-with","title":"Learning Video Instance Segmentation with Recurrent Graph Neural Networks","date":"2020-12-07","arxiv_id":"2012.03911","repositories_listed":0,"syntology":null},{"url":null,"slug":"ncgnn-node-level-capsule-graph-neural-network","title":"NCGNN: Node-Level Capsule Graph Neural Network for Semisupervised Classification","date":"2020-12-07","arxiv_id":"2012.03476","repositories_listed":0,"syntology":null},{"url":null,"slug":"graphpb-graphical-representations-of-prosody","title":"GraphPB: Graphical Representations of Prosody Boundary in Speech Synthesis","date":"2020-12-03","arxiv_id":"2012.02626","repositories_listed":0,"syntology":null},{"url":null,"slug":"sb-mtl-score-based-meta-transfer-learning-for","title":"SB-MTL: Score-based Meta Transfer-Learning for Cross-Domain Few-Shot Learning","date":"2020-12-03","arxiv_id":"2012.01784","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-contextual-alignment-enhanced-cross-graph","title":"A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-text-classification-with-edge","title":"Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-graph-networks-for-compositional","title":"Multimodal Graph Networks for Compositional Generalization in Visual Question Answering","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"random-walk-graph-neural-networks","title":"Random Walk Graph Neural Networks","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-joint-k-node-graph-1","title":"Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-based-learning-on-molecular","title":"Attention-Based Learning on Molecular Ensembles","date":"2020-11-25","arxiv_id":"2011.12820","repositories_listed":0,"syntology":null},{"url":null,"slug":"making-graph-neural-networks-worth-it-for-low","title":"Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning","date":"2020-11-24","arxiv_id":"2011.12203","repositories_listed":0,"syntology":null},{"url":null,"slug":"trust-but-verify-assigning-prediction","title":"Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning","date":"2020-11-24","arxiv_id":"2011.12344","repositories_listed":0,"syntology":null},{"url":null,"slug":"autograph-automated-graph-neural-network","title":"AutoGraph: Automated Graph Neural Network","date":"2020-11-23","arxiv_id":"2011.11288","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-federated-relational-data-modeling","title":"Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty","date":"2020-11-23","arxiv_id":"2011.11369","repositories_listed":0,"syntology":null},{"url":null,"slug":"meg-multi-evidence-gnn-for-multimodal","title":"MEG: Multi-Evidence GNN for Multimodal Semantic Forensics","date":"2020-11-23","arxiv_id":"2011.11286","repositories_listed":0,"syntology":null},{"url":null,"slug":"graphspy-fused-program-semantic-level","title":"GRAPHSPY: Fused Program Semantic-Level Embedding via Graph Neural Networks for Dead Store Detection","date":"2020-11-18","arxiv_id":"2011.09501","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-phase-behavior-in-active","title":"Machine Learning for Phase Behavior in Active Matter Systems","date":"2020-11-18","arxiv_id":"2011.09458","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-graph-neural-networks-to-reconstruct","title":"Using Graph Neural Networks to Reconstruct Ancient Documents","date":"2020-11-13","arxiv_id":"2011.07048","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-oes-viewpoint-invariant-object-factorized","title":"3D-OES: Viewpoint-Invariant Object-Factorized Environment Simulators","date":"2020-11-12","arxiv_id":"2011.06464","repositories_listed":0,"syntology":null},{"url":null,"slug":"generation-of-human-aware-navigation-maps","title":"Generation of Human-aware Navigation Maps using Graph Neural Networks","date":"2020-11-10","arxiv_id":"2011.05180","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-networks-for-distributed-linear","title":"Graph Neural Networks for Distributed Linear-Quadratic Control","date":"2020-11-10","arxiv_id":"2011.05360","repositories_listed":0,"syntology":null},{"url":null,"slug":"asfgnn-automated-separated-federated-graph","title":"ASFGNN: Automated Separated-Federated Graph Neural Network","date":"2020-11-06","arxiv_id":"2011.03248","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-power-control-for-cellular-systems","title":"Learning Power Control for Cellular Systems with Heterogeneous Graph Neural Network","date":"2020-11-06","arxiv_id":"2011.03164","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-learning-for-electronic-structure","title":"Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces","date":"2020-11-05","arxiv_id":"2011.02680","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-self-distilling-graph-neural-network","title":"On Self-Distilling Graph Neural Network","date":"2020-11-04","arxiv_id":"2011.02255","repositories_listed":0,"syntology":null},{"url":null,"slug":"gain-graph-attention-interaction-network-for","title":"GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs","date":"2020-11-03","arxiv_id":"2011.01393","repositories_listed":0,"syntology":null},{"url":null,"slug":"sampling-and-recovery-of-graph-signals-based","title":"Sampling and Recovery of Graph Signals based on Graph Neural Networks","date":"2020-11-03","arxiv_id":"2011.01412","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-accurate-electronic-health","title":"Generating Accurate Electronic Health Assessment from Medical Graph","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-graph-alignment-with-entity-pair","title":"Knowledge Graph Alignment with Entity-Pair Embedding","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"q-can-knowledge-graphs-be-used-to-answer","title":"Q. Can Knowledge Graphs be used to Answer Boolean Questions? A. It’s complicated!","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"text-graph-transformer-for-document","title":"Text Graph Transformer for Document Classification","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"watermarking-graph-neural-networks-by-random","title":"Watermarking Graph Neural Networks by Random Graphs","date":"2020-11-01","arxiv_id":"2011.00512","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-network-for-metal-organic","title":"Graph Neural Network for Metal Organic Framework Potential Energy Approximation","date":"2020-10-29","arxiv_id":"2010.15908","repositories_listed":0,"syntology":null},{"url":null,"slug":"co-embedding-of-nodes-and-edges-with-graph","title":"Co-embedding of Nodes and Edges with Graph Neural Networks","date":"2020-10-25","arxiv_id":"2010.13242","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-heterogeneous-graph-neural","title":"Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing","date":"2020-10-25","arxiv_id":"2010.13080","repositories_listed":0,"syntology":null},{"url":null,"slug":"road-accident-proneness-indicator-based-on","title":"Road Accident Proneness Indicator Based On Time, Weather And Location Specificity Using Graph Neural Networks","date":"2020-10-24","arxiv_id":"2010.12953","repositories_listed":0,"syntology":null},{"url":null,"slug":"graphspeech-syntax-aware-graph-attention","title":"GraphSpeech: Syntax-Aware Graph Attention Network For Neural Speech Synthesis","date":"2020-10-23","arxiv_id":"2010.12423","repositories_listed":0,"syntology":null},{"url":null,"slug":"ngat4rec-neighbor-aware-graph-attention","title":"NGAT4Rec: Neighbor-Aware Graph Attention Network For Recommendation","date":"2020-10-23","arxiv_id":"2010.12256","repositories_listed":0,"syntology":null},{"url":null,"slug":"transferable-graph-optimizers-for-ml","title":"Transferable Graph Optimizers for ML Compilers","date":"2020-10-21","arxiv_id":"2010.12438","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-transfer-for-efficient-on-device","title":"Knowledge Transfer for Efficient On-device False Trigger Mitigation","date":"2020-10-20","arxiv_id":"2010.10591","repositories_listed":0,"syntology":null},{"url":null,"slug":"neuralizing-efficient-higher-order-belief","title":"Neuralizing Efficient Higher-order Belief Propagation","date":"2020-10-19","arxiv_id":"2010.09283","repositories_listed":0,"syntology":null},{"url":"/paper/star-graph-neural-networks-for-session-based","slug":"star-graph-neural-networks-for-session-based","title":"Star Graph Neural Networks for Session-based Recommendation","date":"2020-10-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fgagt-flow-guided-adaptive-graph-tracking","title":"Tracklets Predicting Based Adaptive Graph Tracking","date":"2020-10-18","arxiv_id":"2010.09015","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-path-free-semi-supervised-learning-for","title":"Meta-path Free Semi-supervised Learning for Heterogeneous Networks","date":"2020-10-18","arxiv_id":"2010.08924","repositories_listed":0,"syntology":null},{"url":null,"slug":"decentralized-knowledge-graph-representation-1","title":"Distributed Representations of Entities in Open-World Knowledge Graphs","date":"2020-10-16","arxiv_id":"2010.08114","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalizable-machine-learning-in","title":"Generalizable Machine Learning in Neuroscience using Graph Neural Networks","date":"2020-10-16","arxiv_id":"2010.08569","repositories_listed":0,"syntology":null},{"url":"/paper/manifold-net-using-manifold-learning-for","slug":"manifold-net-using-manifold-learning-for","title":"PointManifold: Using Manifold Learning for Point Cloud Classification","date":"2020-10-14","arxiv_id":"2010.07215","repositories_listed":0,"syntology":null},{"url":null,"slug":"rotation-averaging-with-attention-graph","title":"Rotation Averaging with Attention Graph Neural Networks","date":"2020-10-14","arxiv_id":"2010.06773","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-extractive-text-summarization-with","title":"Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks","date":"2020-10-13","arxiv_id":"2010.06253","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-collaborative-filtering-with-graph","title":"Temporal Collaborative Filtering with Graph Convolutional Neural Networks","date":"2020-10-13","arxiv_id":"2010.06425","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-graph-neural-network-approach-for-scalable-1","title":"A Graph Neural Network Approach for Scalable and Dynamic IP Similarity in Enterprise Networks","date":"2020-10-09","arxiv_id":"2010.04777","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-graph-attention-on-heterogeneous-graph","title":"Meta Graph Attention on Heterogeneous Graph with Node-Edge Co-evolution","date":"2020-10-09","arxiv_id":"2010.04554","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-joint-k-node-graph","title":"Unsupervised Joint $k$-node Graph Representations with Compositional Energy-Based Models","date":"2020-10-08","arxiv_id":"2010.04259","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-colon-cancer-grading-with-graph","title":"Efficient Colon Cancer Grading with Graph Neural Networks","date":"2020-10-02","arxiv_id":"2010.01091","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-based-graph-neural-network-with","title":"Attention-Based Graph Neural Network with Global Context Awareness for Document Understanding","date":"2020-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ji-yu-tu-shen-jing-wang-luo-de-yi-yu-yi-cun","title":"基于图神经网络的汉语依存分析和语义组合计算联合模型(Joint Learning Chinese Dependency Parsing and Semantic Composition based on Graph Neural Network)","date":"2020-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-general-approach-for-identifying","title":"A general approach for identifying hierarchical symmetry constraints for analog circuit layout","date":"2020-09-30","arxiv_id":"2010.00051","repositories_listed":0,"syntology":null},{"url":null,"slug":"ews-gcn-edge-weight-shared-graph","title":"EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data","date":"2020-09-30","arxiv_id":"2009.14588","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-fast-graph-neural-network-based-method-for","title":"A Fast Graph Neural Network-Based Method for Winner Determination in Multi-Unit Combinatorial Auctions","date":"2020-09-29","arxiv_id":"2009.13697","repositories_listed":0,"syntology":null},{"url":null,"slug":"panrep-universal-node-embeddings-for-1","title":"PanRep: Universal node embeddings for heterogeneous graphs","date":"2020-09-28","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"interaction-based-trajectory-prediction-over","title":"Interaction-Based Trajectory Prediction Over a Hybrid Traffic Graph","date":"2020-09-27","arxiv_id":"2009.12916","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-induction-of-value-iteration","title":"Graph neural induction of value iteration","date":"2020-09-26","arxiv_id":"2009.12604","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-context-integrated-relational-spatio","title":"A Context Integrated Relational Spatio-Temporal Model for Demand and Supply Forecasting","date":"2020-09-25","arxiv_id":"2009.12469","repositories_listed":0,"syntology":null},{"url":null,"slug":"sia-gcn-a-spatial-information-aware-graph","title":"SIA-GCN: A Spatial Information Aware Graph Neural Network with 2D Convolutions for Hand Pose Estimation","date":"2020-09-25","arxiv_id":"2009.12473","repositories_listed":0,"syntology":null},{"url":null,"slug":"drug-repurposing-for-covid-19-using-graph","title":"Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence","date":"2020-09-23","arxiv_id":"2009.10931","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-aware-attention-graph-neural","title":"Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks","date":"2020-09-22","arxiv_id":"2009.10235","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-resolution-graph-neural-network-for-1","title":"Multi-Resolution Graph Neural Network for Large-Scale Pointcloud Segmentation","date":"2020-09-18","arxiv_id":"2009.08924","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-edge-weights-in-graph-neural-networks","title":"Dynamic Edge Weights in Graph Neural Networks for 3D Object Detection","date":"2020-09-17","arxiv_id":"2009.08253","repositories_listed":0,"syntology":null},{"url":null,"slug":"layer-stacked-attention-for-heterogeneous","title":"Layer-stacked Attention for Heterogeneous Network Embedding","date":"2020-09-17","arxiv_id":"2009.08072","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-dynamic-gnn-with-secure-aggregation","title":"Federated Dynamic GNN with Secure Aggregation","date":"2020-09-15","arxiv_id":"2009.07351","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-network-based-service-function","title":"Graph Neural Network based Service Function Chaining for Automatic Network Control","date":"2020-09-11","arxiv_id":"2009.05240","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-coreference-resolution-by","title":"Improving Coreference Resolution by Leveraging Entity-Centric Features with Graph Neural Networks and Second-order Inference","date":"2020-09-10","arxiv_id":"2009.04639","repositories_listed":0,"syntology":null},{"url":null,"slug":"fully-convolutional-graph-neural-networks-for","title":"Fully Convolutional Graph Neural Networks for Parametric Virtual Try-On","date":"2020-09-09","arxiv_id":"2009.04592","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-on-job-shop-scheduling","title":"Graph neural networks-based Scheduler for Production planning problems using Reinforcement Learning","date":"2020-09-08","arxiv_id":"2009.03836","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-of-causal-structures-with","title":"Active Learning of Causal Structures with Deep Reinforcement Learning","date":"2020-09-07","arxiv_id":"2009.03009","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-cold-start-in-recommender-systems","title":"Addressing Cold Start in Recommender Systems with Hierarchical Graph Neural Networks","date":"2020-09-07","arxiv_id":"2009.03455","repositories_listed":0,"syntology":null},{"url":null,"slug":"cagnn-cluster-aware-graph-neural-networks-for","title":"CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning","date":"2020-09-03","arxiv_id":"2009.01674","repositories_listed":0,"syntology":null},{"url":null,"slug":"heterogeneous-graph-neural-network-for","title":"Heterogeneous Graph Neural Network for Recommendation","date":"2020-09-02","arxiv_id":"2009.00799","repositories_listed":0,"syntology":null},{"url":null,"slug":"lymph-node-gross-tumor-volume-detection-in","title":"Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network","date":"2020-08-29","arxiv_id":"2008.13013","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-graph-neural-network-for-traffic","title":"Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks","date":"2020-08-28","arxiv_id":"2008.12767","repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-training-of-graph-neural-network-for","title":"Pre-training of Graph Neural Network for Modeling Effects of Mutations on Protein-Protein Binding Affinity","date":"2020-08-28","arxiv_id":"2008.12473","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-network-architecture-search-for","title":"Graph Neural Network Architecture Search for Molecular Property Prediction","date":"2020-08-27","arxiv_id":"2008.12187","repositories_listed":0,"syntology":null},{"url":null,"slug":"featgraph-a-flexible-and-efficient-backend","title":"FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems","date":"2020-08-26","arxiv_id":"2008.11359","repositories_listed":0,"syntology":null}],"record_sha256":"145b05a932e82aab3214de100db309d0dc9abbed59aba5d28eb44d751660c603","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}