{"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":"/method/graph-neural-network/papers/22","list_of":"/method/graph-neural-network","method":"Graph Neural Network","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":22,"pages_in_order":27,"rows_per_page":100,"rows":[2101,2200],"of":2694,"counts":{"archive_papers_tagged":2694,"with_a_code_link":1137,"where_syntology_ran_a_sample":271,"not_listed_spam_title":0,"listed":2694,"listed_where_code_ran":271,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":235,"every_run_a_failure_of_syntologys_instrument":36,"listed_with_a_run_with_no_instrument_failure":235,"listed_every_run_a_failure_of_syntologys_instrument":36,"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":"/method/graph-neural-network","prev":"/method/graph-neural-network/papers/21","next":"/method/graph-neural-network/papers/23","papers":[{"paper":null,"slug":"the-role-of-entropy-in-guiding-a-connection","title":"The Role of Entropy in Guiding a Connection Prover","date":"2021-05-31","arxiv_id":"2105.14706","n_code_links":0,"syntology":null},{"paper":null,"slug":"dagnn-demand-aware-graph-neural-networks-for","title":"DAGNN: Demand-aware Graph Neural Networks for Session-based Recommendation","date":"2021-05-30","arxiv_id":"2105.14428","n_code_links":0,"syntology":null},{"paper":null,"slug":"generating-adversarial-examples-with-graph","title":"Generating Adversarial Examples with Graph Neural Networks","date":"2021-05-30","arxiv_id":"2105.14644","n_code_links":0,"syntology":null},{"paper":null,"slug":"gina-neural-relational-inference-from","title":"GINA: Neural Relational Inference From Independent Snapshots","date":"2021-05-29","arxiv_id":"2105.14329","n_code_links":0,"syntology":null},{"paper":"/paper/hashing-accelerated-graph-neural-networks-for","slug":"hashing-accelerated-graph-neural-networks-for","title":"Hashing-Accelerated Graph Neural Networks for Link Prediction","date":"2021-05-29","arxiv_id":"2105.14280","n_code_links":1,"syntology":null},{"paper":"/paper/neural-enhanced-belief-propagation-for","slug":"neural-enhanced-belief-propagation-for","title":"Neural Enhanced Belief Propagation for Cooperative Localization","date":"2021-05-27","arxiv_id":"2105.12903","n_code_links":1,"syntology":null},{"paper":"/paper/local-global-and-scale-dependent-node-roles","slug":"local-global-and-scale-dependent-node-roles","title":"Local, global and scale-dependent node roles","date":"2021-05-26","arxiv_id":"2105.12598","n_code_links":1,"syntology":null},{"paper":null,"slug":"motif-prediction-with-graph-neural-networks","title":"Motif Prediction with Graph Neural Networks","date":"2021-05-26","arxiv_id":"2106.00761","n_code_links":0,"syntology":null},{"paper":null,"slug":"adagcn-adaptive-boosting-algorithm-for-graph","title":"Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification","date":"2021-05-25","arxiv_id":"2105.11625","n_code_links":0,"syntology":null},{"paper":"/paper/graphfm-graph-factorization-machines-for","slug":"graphfm-graph-factorization-machines-for","title":"GraphFM: Graph Factorization Machines for Feature Interaction Modeling","date":"2021-05-25","arxiv_id":"2105.11866","n_code_links":1,"syntology":null},{"paper":"/paper/dorylus-affordable-scalable-and-accurate-gnn","slug":"dorylus-affordable-scalable-and-accurate-gnn","title":"Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads","date":"2021-05-24","arxiv_id":"2105.11118","n_code_links":1,"syntology":null},{"paper":"/paper/heterogeneous-graph-representation-learning","slug":"heterogeneous-graph-representation-learning","title":"Heterogeneous Graph Representation Learning with Relation Awareness","date":"2021-05-24","arxiv_id":"2105.11122","n_code_links":1,"syntology":null},{"paper":null,"slug":"testrank-bringing-order-into-unlabeled-test","title":"TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks","date":"2021-05-21","arxiv_id":"2105.10113","n_code_links":0,"syntology":null},{"paper":"/paper/superpixel-based-domain-knowledge-infusion-in","slug":"superpixel-based-domain-knowledge-infusion-in","title":"Superpixel-based Knowledge Infusion in Deep Neural Networks for Image Classification","date":"2021-05-20","arxiv_id":"2105.09448","n_code_links":1,"syntology":null},{"paper":null,"slug":"combining-gcn-and-transformer-for-chinese","title":"Combining GCN and Transformer for Chinese Grammatical Error Detection","date":"2021-05-19","arxiv_id":"2105.09085","n_code_links":0,"syntology":null},{"paper":null,"slug":"graph-sanitation-with-application-to-node","title":"Graph Sanitation with Application to Node Classification","date":"2021-05-19","arxiv_id":"2105.09384","n_code_links":0,"syntology":null},{"paper":"/paper/graph-neural-networks-for-decentralized-multi-1","slug":"graph-neural-networks-for-decentralized-multi-1","title":"Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection","date":"2021-05-18","arxiv_id":"2105.08601","n_code_links":1,"syntology":null},{"paper":"/paper/zorro-valid-sparse-and-stable-explanations-in","slug":"zorro-valid-sparse-and-stable-explanations-in","title":"Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks","date":"2021-05-18","arxiv_id":"2105.08621","n_code_links":1,"syntology":null},{"paper":"/paper/improving-graph-neural-networks-with-simple","slug":"improving-graph-neural-networks-with-simple","title":"Improving Graph Neural Networks with Simple Architecture Design","date":"2021-05-17","arxiv_id":"2105.07634","n_code_links":1,"syntology":null},{"paper":"/paper/tcl-transformer-based-dynamic-graph-modelling","slug":"tcl-transformer-based-dynamic-graph-modelling","title":"TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning","date":"2021-05-17","arxiv_id":"2105.07944","n_code_links":3,"syntology":null},{"paper":null,"slug":"neighbourhood-guided-feature-reconstruction","title":"Neighbourhood-guided Feature Reconstruction for Occluded Person Re-Identification","date":"2021-05-16","arxiv_id":"2105.07345","n_code_links":0,"syntology":null},{"paper":"/paper/learning-unknown-from-correlations-graph","slug":"learning-unknown-from-correlations-graph","title":"Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction","date":"2021-05-14","arxiv_id":"2105.06709","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","official":{"repos":["lvguofeng/GNN_PPI"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"cross-domain-contract-element-extraction-with","title":"Cross-Domain Contract Element Extraction with a Bi-directional Feedback Clause-Element Relation Network","date":"2021-05-13","arxiv_id":"2105.06083","n_code_links":0,"syntology":null},{"paper":"/paper/vsr-a-unified-framework-for-document-layout","slug":"vsr-a-unified-framework-for-document-layout","title":"VSR: A Unified Framework for Document Layout Analysis combining Vision, Semantics and Relations","date":"2021-05-13","arxiv_id":"2105.06220","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-graph-neural-network-approach-for-product","title":"A Graph Neural Network Approach for Product Relationship Prediction","date":"2021-05-12","arxiv_id":"2105.05881","n_code_links":0,"syntology":null},{"paper":null,"slug":"hierarchical-graph-neural-networks","title":"Hierarchical Graph Neural Networks","date":"2021-05-07","arxiv_id":"2105.03388","n_code_links":0,"syntology":null},{"paper":null,"slug":"graph-based-multilingual-product-retrieval-in","title":"Graph-based Multilingual Product Retrieval in E-commerce Search","date":"2021-05-06","arxiv_id":"2105.02978","n_code_links":0,"syntology":null},{"paper":"/paper/leveraging-third-order-features-in-skeleton","slug":"leveraging-third-order-features-in-skeleton","title":"Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action Recognition","date":"2021-05-04","arxiv_id":"2105.01563","n_code_links":1,"syntology":null},{"paper":"/paper/an-effective-self-supervised-framework-for","slug":"an-effective-self-supervised-framework-for","title":"An effective self-supervised framework for learning expressive molecular global representations to drug discovery","date":"2021-05-03","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/recovering-barabsi-albert-parameters-of","slug":"recovering-barabsi-albert-parameters-of","title":"Recovering Barabási-Albert Parameters of Graphs through Disentanglement","date":"2021-05-03","arxiv_id":"2105.00997","n_code_links":1,"syntology":null},{"paper":null,"slug":"fast-power-control-adaptation-via-meta","title":"Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks","date":"2021-05-02","arxiv_id":"2105.00459","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-computational-framework-for-modeling","title":"A Computational Framework for Modeling Complex Sensor Network Data Using Graph Signal Processing and Graph Neural Networks in Structural Health Monitoring","date":"2021-05-01","arxiv_id":"2105.05316","n_code_links":0,"syntology":null},{"paper":"/paper/bermuda-triangles-gnns-fail-to-detect-simple","slug":"bermuda-triangles-gnns-fail-to-detect-simple","title":"Bermuda Triangles: GNNs Fail to Detect Simple Topological Structures","date":"2021-05-01","arxiv_id":"2105.00134","n_code_links":1,"syntology":null},{"paper":"/paper/communication-topology-co-design-in-graph","slug":"communication-topology-co-design-in-graph","title":"Communication Topology Co-Design in Graph Recurrent Neural Network Based Distributed Control","date":"2021-04-28","arxiv_id":"2104.13868","n_code_links":1,"syntology":null},{"paper":null,"slug":"building-gan-graph-conditioned-architectural","title":"Building-GAN: Graph-Conditioned Architectural Volumetric Design Generation","date":"2021-04-27","arxiv_id":"2104.13316","n_code_links":0,"syntology":null},{"paper":"/paper/graph-neural-networks-with-adaptive-frequency","slug":"graph-neural-networks-with-adaptive-frequency","title":"AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter","date":"2021-04-26","arxiv_id":"2104.12840","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-latent-graph-dynamics-for-deformable","title":"Learning Latent Graph Dynamics for Visual Manipulation of Deformable Objects","date":"2021-04-25","arxiv_id":"2104.12149","n_code_links":0,"syntology":null},{"paper":null,"slug":"joint-detection-and-localization-of-stealth","title":"Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networks","date":"2021-04-24","arxiv_id":"2104.11846","n_code_links":0,"syntology":null},{"paper":"/paper/graghvqa-language-guided-graph-neural","slug":"graghvqa-language-guided-graph-neural","title":"GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering","date":"2021-04-20","arxiv_id":"2104.10283","n_code_links":1,"syntology":null},{"paper":null,"slug":"mixed-curvature-multi-relational-graph-neural","title":"Mixed-Curvature Multi-Relational Graph Neural Network for Knowledge Graph Completion","date":"2021-04-19","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/pick-and-choose-a-gnn-based-imbalanced","slug":"pick-and-choose-a-gnn-based-imbalanced","title":"Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection","date":"2021-04-19","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/automated-seizure-detection-and-seizure-type","slug":"automated-seizure-detection-and-seizure-type","title":"Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis","date":"2021-04-16","arxiv_id":"2104.08336","n_code_links":1,"syntology":{"ran":3,"of":5,"n_ran_checked":2,"n_instrument":1,"unverified":2,"pointer_only":5,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["tsy935/eeg-gnn-ssl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/higher-order-attribute-enhancing","slug":"higher-order-attribute-enhancing","title":"Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks","date":"2021-04-16","arxiv_id":"2104.07892","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["RingBDStack/HAE"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/improving-hyper-relational-knowledge-graph","slug":"improving-hyper-relational-knowledge-graph","title":"Improving Hyper-Relational Knowledge Graph Completion","date":"2021-04-16","arxiv_id":"2104.08167","n_code_links":1,"syntology":null},{"paper":"/paper/reinforced-neighborhood-selection-guided","slug":"reinforced-neighborhood-selection-guided","title":"Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks","date":"2021-04-16","arxiv_id":"2104.07886","n_code_links":1,"syntology":null},{"paper":null,"slug":"accurate-prediction-of-free-solvation-energy","title":"Accurate Prediction of Free Solvation Energy of Organic Molecules via Graph Attention Network and Message Passing Neural Network from Pairwise Atomistic Interactions","date":"2021-04-15","arxiv_id":"2105.02048","n_code_links":0,"syntology":null},{"paper":"/paper/dynamic-graph-neural-networks-for-sequential","slug":"dynamic-graph-neural-networks-for-sequential","title":"Dynamic Graph Neural Networks for Sequential Recommendation","date":"2021-04-15","arxiv_id":"2104.07368","n_code_links":1,"syntology":null},{"paper":"/paper/identity-inference-on-blockchain-using-graph","slug":"identity-inference-on-blockchain-using-graph","title":"Identity Inference on Blockchain using Graph Neural Network","date":"2021-04-14","arxiv_id":"2104.06559","n_code_links":1,"syntology":null},{"paper":"/paper/glara-graph-based-labeling-rule-augmentation","slug":"glara-graph-based-labeling-rule-augmentation","title":"GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition","date":"2021-04-13","arxiv_id":"2104.06230","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["zhaoxy92/GLaRA"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"which-hyperparameters-to-optimise-an","title":"Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network For Molecular Property Prediction","date":"2021-04-13","arxiv_id":"2104.06046","n_code_links":0,"syntology":null},{"paper":null,"slug":"equivariant-geometric-learning-for-digital","title":"Equivariant geometric learning for digital rock physics: estimating formation factor and effective permeability tensors from Morse graph","date":"2021-04-12","arxiv_id":"2104.05608","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-representation-learning-for-scientific","title":"On Representation Learning for Scientific News Articles Using Heterogeneous Knowledge Graphs","date":"2021-04-12","arxiv_id":"2104.05866","n_code_links":0,"syntology":null},{"paper":"/paper/scalable-power-control-beamforming-in","slug":"scalable-power-control-beamforming-in","title":"Scalable Power Control/Beamforming in Heterogeneous Wireless Networks with Graph Neural Networks","date":"2021-04-12","arxiv_id":"2104.05463","n_code_links":1,"syntology":null},{"paper":"/paper/pyramidal-reservoir-graph-neural-network","slug":"pyramidal-reservoir-graph-neural-network","title":"Pyramidal Reservoir Graph Neural Network","date":"2021-04-10","arxiv_id":"2104.04710","n_code_links":2,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"0 ran · 2 unverified","official":{"repos":["FilippoMB/Benchmark_dataset_for_graph_classification","FilippoMB/Pyramidal-Reservoir-Graph-Nerual-Networks"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":null,"slug":"qd-gcn-query-driven-graph-convolutional","title":"Query Driven-Graph Neural Networks for Community Search: From Non-Attributed, Attributed, to Interactive Attributed","date":"2021-04-08","arxiv_id":"2104.03583","n_code_links":0,"syntology":null},{"paper":"/paper/attentional-graph-neural-network-for-parking","slug":"attentional-graph-neural-network-for-parking","title":"Attentional Graph Neural Network for Parking-slot Detection","date":"2021-04-06","arxiv_id":"2104.02576","n_code_links":1,"syntology":null},{"paper":null,"slug":"hyperbolic-variational-graph-neural-network","title":"Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs","date":"2021-04-06","arxiv_id":"2104.02228","n_code_links":0,"syntology":null},{"paper":"/paper/learning-spatial-context-with-graph-neural","slug":"learning-spatial-context-with-graph-neural","title":"Learning Spatial Context with Graph Neural Network for Multi-Person Pose Grouping","date":"2021-04-06","arxiv_id":"2104.02385","n_code_links":1,"syntology":null},{"paper":"/paper/visual-camera-re-localization-using-graph","slug":"visual-camera-re-localization-using-graph","title":"Visual Camera Re-Localization Using Graph Neural Networks and Relative Pose Supervision","date":"2021-04-06","arxiv_id":"2104.02538","n_code_links":1,"syntology":null},{"paper":null,"slug":"graph-neural-networks-based-detection-of","title":"Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids","date":"2021-04-05","arxiv_id":"2104.02012","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-the-expressive-power-of-graph","title":"Improving the Expressive Power of Graph Neural Network with Tinhofer Algorithm","date":"2021-04-05","arxiv_id":"2104.01848","n_code_links":0,"syntology":null},{"paper":"/paper/segmentation-of-em-showers-for-neutrino","slug":"segmentation-of-em-showers-for-neutrino","title":"Segmentation of EM showers for neutrino experiments with deep graph neural networks","date":"2021-04-05","arxiv_id":"2104.02040","n_code_links":1,"syntology":null},{"paper":"/paper/mgn-net-a-multi-view-graph-normalizer-for","slug":"mgn-net-a-multi-view-graph-normalizer-for","title":"MGN-Net: a multi-view graph normalizer for integrating heterogeneous biological network populations","date":"2021-04-04","arxiv_id":"2104.03895","n_code_links":1,"syntology":null},{"paper":"/paper/adaptive-filters-and-aggregator-fusion-for","slug":"adaptive-filters-and-aggregator-fusion-for","title":"Do We Need Anisotropic Graph Neural Networks?","date":"2021-04-03","arxiv_id":"2104.01481","n_code_links":2,"syntology":null},{"paper":null,"slug":"topological-regularization-for-graph-neural","title":"Topological Regularization for Graph Neural Networks Augmentation","date":"2021-04-03","arxiv_id":"2104.02478","n_code_links":0,"syntology":null},{"paper":"/paper/bipartite-graph-network-with-adaptive-message","slug":"bipartite-graph-network-with-adaptive-message","title":"Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation","date":"2021-04-01","arxiv_id":"2104.00308","n_code_links":4,"syntology":null},{"paper":"/paper/qubit-routing-using-graph-neural-network","slug":"qubit-routing-using-graph-neural-network","title":"Qubit Routing using Graph Neural Network aided Monte Carlo Tree Search","date":"2021-04-01","arxiv_id":"2104.01992","n_code_links":1,"syntology":null},{"paper":null,"slug":"structural-encoding-and-pre-training-matter","title":"Structural Encoding and Pre-training Matter: Adapting BERT for Table-Based Fact Verification","date":"2021-04-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"context-aware-short-term-interest-first-model","title":"Context-aware short-term interest first model for session-based recommendation","date":"2021-03-29","arxiv_id":"2103.15514","n_code_links":0,"syntology":null},{"paper":"/paper/context-modeling-in-3d-human-pose-estimation","slug":"context-modeling-in-3d-human-pose-estimation","title":"Context Modeling in 3D Human Pose Estimation: A Unified Perspective","date":"2021-03-29","arxiv_id":"2103.15507","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","official":null}},{"paper":null,"slug":"graph-classification-by-mixture-of-diverse","title":"Graph Classification by Mixture of Diverse Experts","date":"2021-03-29","arxiv_id":"2103.15622","n_code_links":0,"syntology":null},{"paper":null,"slug":"joint-user-association-and-power-allocation","title":"Joint User Association and Power Allocation in Heterogeneous Ultra Dense Network via Semi-Supervised Representation Learning","date":"2021-03-29","arxiv_id":"2103.15367","n_code_links":0,"syntology":null},{"paper":null,"slug":"unified-graph-structured-models-for-video","title":"Unified Graph Structured Models for Video Understanding","date":"2021-03-29","arxiv_id":"2103.15662","n_code_links":0,"syntology":null},{"paper":null,"slug":"insertgnn-can-graph-neural-networks","title":"InsertGNN: Can Graph Neural Networks Outperform Humans in TOEFL Sentence Insertion Problem?","date":"2021-03-28","arxiv_id":"2103.15066","n_code_links":0,"syntology":null},{"paper":"/paper/self-supervised-graph-neural-networks-without","slug":"self-supervised-graph-neural-networks-without","title":"Self-supervised Graph Neural Networks without explicit negative sampling","date":"2021-03-27","arxiv_id":"2103.14958","n_code_links":2,"syntology":null},{"paper":null,"slug":"preserve-promote-or-attack-gnn-explanation","title":"Preserve, Promote, or Attack? GNN Explanation via Topology Perturbation","date":"2021-03-25","arxiv_id":"2103.13944","n_code_links":0,"syntology":null},{"paper":"/paper/degraphcs-embedding-variable-based-flow-graph","slug":"degraphcs-embedding-variable-based-flow-graph","title":"deGraphCS: Embedding Variable-based Flow Graph for Neural Code Search","date":"2021-03-24","arxiv_id":"2103.13020","n_code_links":1,"syntology":null},{"paper":null,"slug":"complex-factoid-question-answering-with-a","title":"Complex Factoid Question Answering with a Free-Text Knowledge Graph","date":"2021-03-23","arxiv_id":"2103.12876","n_code_links":0,"syntology":null},{"paper":"/paper/mars-markov-molecular-sampling-for-multi-1","slug":"mars-markov-molecular-sampling-for-multi-1","title":"MARS: Markov Molecular Sampling for Multi-objective Drug Discovery","date":"2021-03-18","arxiv_id":"2103.10432","n_code_links":1,"syntology":null},{"paper":"/paper/dual-side-deep-context-aware-modulation-for","slug":"dual-side-deep-context-aware-modulation-for","title":"Dual Side Deep Context-aware Modulation for Social Recommendation","date":"2021-03-16","arxiv_id":"2103.08976","n_code_links":1,"syntology":null},{"paper":"/paper/r-gsn-the-relation-based-graph-similar","slug":"r-gsn-the-relation-based-graph-similar","title":"R-GSN: The Relation-based Graph Similar Network for Heterogeneous Graph","date":"2021-03-14","arxiv_id":"2103.07877","n_code_links":1,"syntology":null},{"paper":"/paper/spectral-temporal-graph-neural-network-for-1","slug":"spectral-temporal-graph-neural-network-for-1","title":"Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting","date":"2021-03-13","arxiv_id":"2103.07719","n_code_links":3,"syntology":{"ran":2,"of":13,"n_ran_checked":2,"n_instrument":0,"unverified":11,"pointer_only":2,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 11 unverified","official":{"repos":["WenjieDu/PyPOTS"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"heterogeneous-information-network-based","title":"Heterogeneous Information Network-based Interest Composition with Graph Neural Network for Recommendation","date":"2021-03-11","arxiv_id":"2103.06560","n_code_links":0,"syntology":null},{"paper":"/paper/holistic-3d-scene-understanding-from-a-single-1","slug":"holistic-3d-scene-understanding-from-a-single-1","title":"Holistic 3D Scene Understanding from a Single Image with Implicit Representation","date":"2021-03-11","arxiv_id":"2103.06422","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-sequential-recommendation-with","title":"Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks","date":"2021-03-10","arxiv_id":"2103.05923","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-graph-neural-networks-with-positive","title":"Learning Graph Neural Networks with Positive and Unlabeled Nodes","date":"2021-03-08","arxiv_id":"2103.04683","n_code_links":0,"syntology":null},{"paper":"/paper/fast-interactive-video-object-segmentation","slug":"fast-interactive-video-object-segmentation","title":"Fast Interactive Video Object Segmentation with Graph Neural Networks","date":"2021-03-05","arxiv_id":"2103.03821","n_code_links":1,"syntology":null},{"paper":null,"slug":"nf-gnn-network-flow-graph-neural-networks-for","title":"NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification","date":"2021-03-05","arxiv_id":"2103.03939","n_code_links":0,"syntology":null},{"paper":"/paper/recurrent-graph-neural-network-algorithm-for","slug":"recurrent-graph-neural-network-algorithm-for","title":"Recurrent Graph Neural Network Algorithm for Unsupervised Network Community Detection","date":"2021-03-03","arxiv_id":"2103.02520","n_code_links":1,"syntology":null},{"paper":"/paper/autobahn-automorphism-based-graph-neural-nets","slug":"autobahn-automorphism-based-graph-neural-nets","title":"Autobahn: Automorphism-based Graph Neural Nets","date":"2021-03-02","arxiv_id":"2103.01710","n_code_links":1,"syntology":null},{"paper":"/paper/deep-learning-strategies-for-protodune-raw","slug":"deep-learning-strategies-for-protodune-raw","title":"Deep Learning strategies for ProtoDUNE raw data denoising","date":"2021-03-02","arxiv_id":"2103.01596","n_code_links":1,"syntology":null},{"paper":"/paper/self-supervised-auxiliary-learning-for-graph","slug":"self-supervised-auxiliary-learning-for-graph","title":"Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning","date":"2021-03-01","arxiv_id":"2103.00771","n_code_links":1,"syntology":null},{"paper":"/paper/anomaly-detection-on-attributed-networks-via","slug":"anomaly-detection-on-attributed-networks-via","title":"Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning","date":"2021-02-27","arxiv_id":"2103.00113","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-and-interpretable-robot","title":"Efficient and Interpretable Robot Manipulation with Graph Neural Networks","date":"2021-02-25","arxiv_id":"2102.13177","n_code_links":0,"syntology":null},{"paper":null,"slug":"benchmarking-graph-neural-networks-on-link","title":"Benchmarking Graph Neural Networks on Link Prediction","date":"2021-02-24","arxiv_id":"2102.12557","n_code_links":0,"syntology":null},{"paper":"/paper/pre-training-on-dynamic-graph-neural-networks","slug":"pre-training-on-dynamic-graph-neural-networks","title":"Pre-Training on Dynamic Graph Neural Networks","date":"2021-02-24","arxiv_id":"2102.12380","n_code_links":1,"syntology":null},{"paper":"/paper/hierarchical-cell-to-tissue-graph","slug":"hierarchical-cell-to-tissue-graph","title":"Hierarchical Graph Representations in Digital Pathology","date":"2021-02-22","arxiv_id":"2102.11057","n_code_links":4,"syntology":null},{"paper":"/paper/persistence-homology-for-link-prediction-an","slug":"persistence-homology-for-link-prediction-an","title":"Link Prediction with Persistent Homology: An Interactive View","date":"2021-02-20","arxiv_id":"2102.10255","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["pkuyzy/TLC-GNN"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/a-graph-neural-network-to-model-user-comfort","slug":"a-graph-neural-network-to-model-user-comfort","title":"A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation","date":"2021-02-17","arxiv_id":"2102.08863","n_code_links":3,"syntology":null},{"paper":null,"slug":"contrakg-contrastive-based-transfer-learning","title":"Learning Visual Models using a Knowledge Graph as a Trainer","date":"2021-02-17","arxiv_id":"2102.08747","n_code_links":0,"syntology":null}],"record_sha256":"a5d2410bc5023e38f70dd01d4624e5344988ddc45fd822a2de69a1dd1c458d8a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}