{"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/gcn/papers/8","list_of":"/method/gcn","method":"GCN","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":8,"pages_in_order":10,"rows_per_page":100,"rows":[701,800],"of":968,"counts":{"archive_papers_tagged":968,"with_a_code_link":443,"where_syntology_ran_a_sample":94,"not_listed_spam_title":0,"listed":968,"listed_where_code_ran":94,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":79,"every_run_a_failure_of_syntologys_instrument":15,"listed_with_a_run_with_no_instrument_failure":79,"listed_every_run_a_failure_of_syntologys_instrument":15,"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/gcn","prev":"/method/gcn/papers/7","next":"/method/gcn/papers/9","papers":[{"paper":"/paper/lightweight-dynamic-graph-convolutional","slug":"lightweight-dynamic-graph-convolutional","title":"Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation","date":"2020-10-09","arxiv_id":"2010.04383","n_code_links":1,"syntology":null},{"paper":"/paper/data-driven-learning-of-geometric-scattering-1","slug":"data-driven-learning-of-geometric-scattering-1","title":"Data-Driven Learning of Geometric Scattering Networks","date":"2020-10-06","arxiv_id":"2010.02415","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":null}},{"paper":"/paper/a-unified-view-on-graph-neural-networks-as-1","slug":"a-unified-view-on-graph-neural-networks-as-1","title":"A Unified View on Graph Neural Networks as Graph Signal Denoising","date":"2020-10-05","arxiv_id":"2010.01777","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":["alge24/ADA-UGNN"],"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/resgcn-attention-based-deep-residual-modeling","slug":"resgcn-attention-based-deep-residual-modeling","title":"ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks","date":"2020-09-30","arxiv_id":"2009.14738","n_code_links":1,"syntology":null},{"paper":null,"slug":"uncertainty-matching-graph-neural-networks-to","title":"Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks","date":"2020-09-30","arxiv_id":"2009.14455","n_code_links":0,"syntology":null},{"paper":"/paper/new-gcnn-based-architecture-for-semi","slug":"new-gcnn-based-architecture-for-semi","title":"Semi-Supervised Node Classification by Graph Convolutional Networks and Extracted Side Information","date":"2020-09-29","arxiv_id":"2009.13734","n_code_links":1,"syntology":null},{"paper":"/paper/graph-neural-networks-with-heterophily","slug":"graph-neural-networks-with-heterophily","title":"Graph Neural Networks with Heterophily","date":"2020-09-28","arxiv_id":"2009.13566","n_code_links":1,"syntology":null},{"paper":null,"slug":"revisiting-graph-convolutional-network-on","title":"Revisiting Graph Convolutional Network on Semi-Supervised Node Classification from an Optimization Perspective","date":"2020-09-24","arxiv_id":"2009.11469","n_code_links":0,"syntology":null},{"paper":null,"slug":"explainable-stable-and-scalable-graph","title":"Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation","date":"2020-09-22","arxiv_id":"2009.10367","n_code_links":0,"syntology":null},{"paper":"/paper/a-hybrid-classification-approach-using-topic","slug":"a-hybrid-classification-approach-using-topic","title":"A Hybrid Classification Approach using Topic Modeling and Graph Convolution Networks","date":"2020-09-19","arxiv_id":null,"n_code_links":3,"syntology":null},{"paper":null,"slug":"multi-level-graph-convolutional-network-with","title":"Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification","date":"2020-09-19","arxiv_id":"2009.09196","n_code_links":0,"syntology":null},{"paper":null,"slug":"image-retrieval-for-structure-from-motion-via","title":"Image Retrieval for Structure-from-Motion via Graph Convolutional Network","date":"2020-09-17","arxiv_id":"2009.08049","n_code_links":0,"syntology":null},{"paper":"/paper/catgcn-graph-convolutional-networks-with","slug":"catgcn-graph-convolutional-networks-with","title":"CatGCN: Graph Convolutional Networks with Categorical Node Features","date":"2020-09-11","arxiv_id":"2009.05303","n_code_links":1,"syntology":null},{"paper":"/paper/devil-s-in-the-detail-graph-based-key-point","slug":"devil-s-in-the-detail-graph-based-key-point","title":"Devil's in the Details: Aligning Visual Clues for Conditional Embedding in Person Re-Identification","date":"2020-09-11","arxiv_id":"2009.05250","n_code_links":1,"syntology":null},{"paper":"/paper/semantic-preserving-reinforcement-learning","slug":"semantic-preserving-reinforcement-learning","title":"Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection","date":"2020-09-11","arxiv_id":"2009.05602","n_code_links":1,"syntology":null},{"paper":"/paper/masked-label-prediction-unified-massage","slug":"masked-label-prediction-unified-massage","title":"Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification","date":"2020-09-08","arxiv_id":"2009.03509","n_code_links":3,"syntology":{"ran":7,"of":7,"n_ran_checked":6,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 2 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["PaddlePaddle/PGL"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"visual-object-tracking-by-segmentation-with","title":"Visual Object Tracking by Segmentation with Graph Convolutional Network","date":"2020-09-05","arxiv_id":"2009.02523","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-supervised-smoothing-graph-neural","title":"SAIL: Self-Augmented Graph Contrastive Learning","date":"2020-09-02","arxiv_id":"2009.00934","n_code_links":0,"syntology":null},{"paper":"/paper/learning-robust-node-representation-on-graphs","slug":"learning-robust-node-representation-on-graphs","title":"Learning Node Representations against Perturbations","date":"2020-08-26","arxiv_id":"2008.11416","n_code_links":1,"syntology":null},{"paper":null,"slug":"graph-convolutional-networks-reveal-neural","title":"Graph Convolutional Networks Reveal Neural Connections Encoding Prosthetic Sensation","date":"2020-08-23","arxiv_id":"2009.03272","n_code_links":0,"syntology":null},{"paper":null,"slug":"tsam-temporal-link-prediction-in-directed","title":"TSAM: Temporal Link Prediction in Directed Networks based on Self-Attention Mechanism","date":"2020-08-23","arxiv_id":"2008.10021","n_code_links":0,"syntology":null},{"paper":null,"slug":"rethinking-and-relieving-over-smoothing-in","title":"Tackling Over-Smoothing for General Graph Convolutional Networks","date":"2020-08-22","arxiv_id":"2008.09864","n_code_links":0,"syntology":null},{"paper":"/paper/mmea-entity-alignment-for-multi-modal","slug":"mmea-entity-alignment-for-multi-modal","title":"MMEA: Entity Alignment for Multi-Modal Knowledge Graphs","date":"2020-08-20","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"training-matters-unlocking-potentials-of","title":"Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks","date":"2020-08-20","arxiv_id":"2008.08838","n_code_links":0,"syntology":null},{"paper":null,"slug":"video-region-annotation-with-sparse-bounding","title":"Video Region Annotation with Sparse Bounding Boxes","date":"2020-08-17","arxiv_id":"2008.07049","n_code_links":0,"syntology":null},{"paper":"/paper/alleviating-human-level-shift-a-robust-domain","slug":"alleviating-human-level-shift-a-robust-domain","title":"Alleviating Human-level Shift : A Robust Domain Adaptation Method for Multi-person Pose Estimation","date":"2020-08-13","arxiv_id":"2008.05717","n_code_links":1,"syntology":null},{"paper":null,"slug":"multivariate-relations-aggregation-learning","title":"Multivariate Relations Aggregation Learning in Social Networks","date":"2020-08-09","arxiv_id":"2008.03654","n_code_links":0,"syntology":null},{"paper":"/paper/richly-activated-graph-convolutional-network","slug":"richly-activated-graph-convolutional-network","title":"Richly Activated Graph Convolutional Network for Robust Skeleton-based Action Recognition","date":"2020-08-09","arxiv_id":"2008.03791","n_code_links":3,"syntology":null},{"paper":"/paper/graph-convolutional-networks-for-1","slug":"graph-convolutional-networks-for-1","title":"Graph Convolutional Networks for Hyperspectral Image Classification","date":"2020-08-06","arxiv_id":"2008.02457","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["danfenghong/IEEE_TGRS_GCN"],"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","unlocated"]}}},{"paper":"/paper/compact-graph-architecture-for-speech-emotion","slug":"compact-graph-architecture-for-speech-emotion","title":"Compact Graph Architecture for Speech Emotion Recognition","date":"2020-08-05","arxiv_id":"2008.02063","n_code_links":3,"syntology":null},{"paper":null,"slug":"generative-ensemble-regression-learning","title":"Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-Informed Deep Generative Models","date":"2020-08-05","arxiv_id":"2008.01915","n_code_links":0,"syntology":null},{"paper":"/paper/pseudoinverse-graph-convolutional-networks","slug":"pseudoinverse-graph-convolutional-networks","title":"Pseudoinverse Graph Convolutional Networks: Fast Filters Tailored for Large Eigengaps of Dense Graphs and Hypergraphs","date":"2020-08-03","arxiv_id":"2008.00720","n_code_links":1,"syntology":null},{"paper":null,"slug":"relation-extraction-with-self-determined","title":"Relation Extraction with Self-determined Graph Convolutional Network","date":"2020-08-02","arxiv_id":"2008.00441","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-skeleton-based-action","title":"Improving Skeleton-based Action Recognitionwith Robust Spatial and Temporal Features","date":"2020-08-01","arxiv_id":"2008.00324","n_code_links":0,"syntology":null},{"paper":null,"slug":"grid2vec-learning-efficient-visual","title":"flexgrid2vec: Learning Efficient Visual Representations Vectors","date":"2020-07-30","arxiv_id":"2007.15444","n_code_links":0,"syntology":null},{"paper":"/paper/mix-dimension-in-poincare-geometry-for-3d","slug":"mix-dimension-in-poincare-geometry-for-3d","title":"Mix Dimension in Poincaré Geometry for 3D Skeleton-based Action Recognition","date":"2020-07-30","arxiv_id":"2007.15678","n_code_links":0,"syntology":null},{"paper":"/paper/dynamic-gcn-context-enriched-topology","slug":"dynamic-gcn-context-enriched-topology","title":"Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition","date":"2020-07-29","arxiv_id":"2007.14690","n_code_links":1,"syntology":null},{"paper":null,"slug":"simplification-of-graph-convolutional","title":"Simplification of Graph Convolutional Networks: A Matrix Factorization-based Perspective","date":"2020-07-17","arxiv_id":"2007.09036","n_code_links":0,"syntology":null},{"paper":"/paper/deep-learning-for-abstract-argumentation","slug":"deep-learning-for-abstract-argumentation","title":"Deep Learning for Abstract Argumentation Semantics","date":"2020-07-15","arxiv_id":"2007.07629","n_code_links":1,"syntology":null},{"paper":null,"slug":"distributed-graph-convolutional-networks","title":"Distributed Training of Graph Convolutional Networks","date":"2020-07-13","arxiv_id":"2007.06281","n_code_links":0,"syntology":null},{"paper":null,"slug":"active-learning-on-attributed-graphs-via","title":"Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation","date":"2020-07-09","arxiv_id":"2007.05003","n_code_links":0,"syntology":null},{"paper":"/paper/graph-convolutional-networks-for-graphs","slug":"graph-convolutional-networks-for-graphs","title":"Graph Convolutional Networks for Graphs Containing Missing Features","date":"2020-07-09","arxiv_id":"2007.04583","n_code_links":2,"syntology":{"ran":6,"of":9,"n_ran_checked":6,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["marblet/GCNmf"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/rgcf-refined-graph-convolution-collaborative","slug":"rgcf-refined-graph-convolution-collaborative","title":"RGCF: Refined Graph Convolution Collaborative Filtering with concise and expressive embedding","date":"2020-07-07","arxiv_id":"2007.03383","n_code_links":1,"syntology":null},{"paper":"/paper/simple-and-deep-graph-convolutional-networks-1","slug":"simple-and-deep-graph-convolutional-networks-1","title":"Simple and Deep Graph Convolutional Networks","date":"2020-07-04","arxiv_id":"2007.02133","n_code_links":4,"syntology":{"ran":5,"of":7,"n_ran_checked":5,"n_instrument":0,"unverified":2,"pointer_only":4,"phrase":"5 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 5 samples that ran constructed an object rather than computing a result","official":{"repos":["chennnM/GCNII"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["found_in_text","listed","official"]}}},{"paper":"/paper/structure-aware-human-action-generation","slug":"structure-aware-human-action-generation","title":"Structure-Aware Human-Action Generation","date":"2020-07-04","arxiv_id":"2007.01971","n_code_links":1,"syntology":null},{"paper":null,"slug":"enhancing-the-association-in-multi-object","title":"Enhancing the Association in Multi-Object Tracking via Neighbor Graph","date":"2020-07-01","arxiv_id":"2007.00265","n_code_links":0,"syntology":null},{"paper":null,"slug":"from-spectrum-wavelet-to-vertex-propagation","title":"From Spectrum Wavelet to Vertex Propagation: Graph Convolutional Networks Based on Taylor Approximation","date":"2020-07-01","arxiv_id":"2007.00730","n_code_links":0,"syntology":null},{"paper":null,"slug":"hgcn4mesh-hybrid-graph-convolution-network","title":"HGCN4MeSH: Hybrid Graph Convolution Network for MeSH Indexing","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"neural-reranking-for-dependency-parsing-an","title":"Neural Reranking for Dependency Parsing: An Evaluation","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"local-neighbor-propagation-embedding","title":"Local Neighbor Propagation Embedding","date":"2020-06-29","arxiv_id":"2006.16009","n_code_links":0,"syntology":null},{"paper":"/paper/graph-convolutional-network-for","slug":"graph-convolutional-network-for","title":"Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters","date":"2020-06-28","arxiv_id":"2006.15516","n_code_links":2,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":{"repos":["Wenhui-Yu/LCFN"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":null,"slug":"graph-convolutional-networks-against-degree","title":"Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks","date":"2020-06-28","arxiv_id":"2006.15643","n_code_links":0,"syntology":null},{"paper":"/paper/graph-structural-topic-neural-network","slug":"graph-structural-topic-neural-network","title":"Graph Structural-topic Neural Network","date":"2020-06-25","arxiv_id":"2006.14278","n_code_links":1,"syntology":null},{"paper":"/paper/data-augmentation-view-on-graph-convolutional","slug":"data-augmentation-view-on-graph-convolutional","title":"Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning","date":"2020-06-23","arxiv_id":"2006.13090","n_code_links":1,"syntology":null},{"paper":null,"slug":"connecting-graph-convolutional-networks-and","title":"Connecting Graph Convolutional Networks and Graph-Regularized PCA","date":"2020-06-22","arxiv_id":"2006.12294","n_code_links":0,"syntology":null},{"paper":"/paper/sequential-graph-convolutional-network-for","slug":"sequential-graph-convolutional-network-for","title":"Sequential Graph Convolutional Network for Active Learning","date":"2020-06-18","arxiv_id":"2006.10219","n_code_links":1,"syntology":null},{"paper":"/paper/relational-fusion-networks-graph","slug":"relational-fusion-networks-graph","title":"Relational Fusion Networks: Graph Convolutional Networks for Road Networks","date":"2020-06-16","arxiv_id":"2006.09030","n_code_links":1,"syntology":null},{"paper":"/paper/generalized-multi-relational-graph","slug":"generalized-multi-relational-graph","title":"Knowledge Embedding Based Graph Convolutional Network","date":"2020-06-12","arxiv_id":"2006.07331","n_code_links":1,"syntology":{"ran":5,"of":12,"n_ran_checked":3,"n_instrument":2,"unverified":7,"pointer_only":4,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 7 unverified","official":{"repos":["Maysir/GEM-GCN"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":7,"ran_from_kinds":["official"]}}},{"paper":"/paper/on-the-bottleneck-of-graph-neural-networks","slug":"on-the-bottleneck-of-graph-neural-networks","title":"On the Bottleneck of Graph Neural Networks and its Practical Implications","date":"2020-06-09","arxiv_id":"2006.05205","n_code_links":3,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 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","official":{"repos":["tech-srl/bottleneck"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/when-differential-privacy-meets-graph-neural","slug":"when-differential-privacy-meets-graph-neural","title":"Locally Private Graph Neural Networks","date":"2020-06-09","arxiv_id":"2006.05535","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":0,"n_instrument":1,"unverified":2,"pointer_only":0,"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) · 2 unverified","official":{"repos":["sisaman/lpgnn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"semantic-graph-enhanced-visual-network-for","title":"Graph-based Visual-Semantic Entanglement Network for Zero-shot Image Recognition","date":"2020-06-08","arxiv_id":"2006.04648","n_code_links":0,"syntology":null},{"paper":null,"slug":"single-layer-graph-convolutional-networks-for","title":"Single-Layer Graph Convolutional Networks For Recommendation","date":"2020-06-07","arxiv_id":"2006.04164","n_code_links":0,"syntology":null},{"paper":"/paper/self-supervised-training-of-graph","slug":"self-supervised-training-of-graph","title":"Self-supervised Training of Graph Convolutional Networks","date":"2020-06-03","arxiv_id":"2006.02380","n_code_links":1,"syntology":null},{"paper":"/paper/skeleton-based-action-recognition-with-shift","slug":"skeleton-based-action-recognition-with-shift","title":"Skeleton-Based Action Recognition With Shift Graph Convolutional Network","date":"2020-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"spatial-temporal-graph-convolutional-network","title":"Spatial-Temporal Graph Convolutional Network for Video-Based Person Re-Identification","date":"2020-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-graph-learning-for-semi-supervised","title":"Deep graph learning for semi-supervised classification","date":"2020-05-29","arxiv_id":"2005.14403","n_code_links":0,"syntology":null},{"paper":"/paper/on-incorporating-structural-information-to","slug":"on-incorporating-structural-information-to","title":"On Incorporating Structural Information to improve Dialogue Response Generation","date":"2020-05-28","arxiv_id":"2005.14315","n_code_links":1,"syntology":null},{"paper":"/paper/interpretable-and-efficient-heterogeneous","slug":"interpretable-and-efficient-heterogeneous","title":"Interpretable and Efficient Heterogeneous Graph Convolutional Network","date":"2020-05-27","arxiv_id":"2005.13183","n_code_links":1,"syntology":null},{"paper":"/paper/mvin-learning-multiview-items-for","slug":"mvin-learning-multiview-items-for","title":"MVIN: Learning Multiview Items for Recommendation","date":"2020-05-26","arxiv_id":"2005.12516","n_code_links":1,"syntology":null},{"paper":null,"slug":"relevant-region-prediction-for-crowd-counting","title":"Relevant Region Prediction for Crowd Counting","date":"2020-05-20","arxiv_id":"2005.09816","n_code_links":0,"syntology":null},{"paper":"/paper/benchmark-tests-of-convolutional-neural","slug":"benchmark-tests-of-convolutional-neural","title":"Benchmark Tests of Convolutional Neural Network and Graph Convolutional Network on HorovodRunner Enabled Spark Clusters","date":"2020-05-12","arxiv_id":"2005.05510","n_code_links":1,"syntology":null},{"paper":"/paper/multi-view-graph-convolutional-networks-for","slug":"multi-view-graph-convolutional-networks-for","title":"Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction","date":"2020-05-11","arxiv_id":"2005.04955","n_code_links":1,"syntology":null},{"paper":null,"slug":"anonymized-gcn-a-novel-robust-graph-embedding","title":"AN-GCN: An Anonymous Graph Convolutional Network Defense Against Edge-Perturbing Attack","date":"2020-05-06","arxiv_id":"2005.03482","n_code_links":0,"syntology":null},{"paper":null,"slug":"pruned-graph-scattering-transforms","title":"Pruned Graph Scattering Transforms","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"a-robust-hierarchical-graph-convolutional","title":"A Robust Hierarchical Graph Convolutional Network Model for Collaborative Filtering","date":"2020-04-30","arxiv_id":"2004.14734","n_code_links":0,"syntology":null},{"paper":"/paper/directed-graph-convolutional-network","slug":"directed-graph-convolutional-network","title":"Directed Graph Convolutional Network","date":"2020-04-29","arxiv_id":"2004.13970","n_code_links":1,"syntology":null},{"paper":"/paper/motion-guided-3d-pose-estimation-from-videos","slug":"motion-guided-3d-pose-estimation-from-videos","title":"Motion Guided 3D Pose Estimation from Videos","date":"2020-04-29","arxiv_id":"2004.13985","n_code_links":1,"syntology":{"ran":5,"of":9,"n_ran_checked":4,"n_instrument":1,"unverified":4,"pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","official":null}},{"paper":"/paper/contextualised-graph-attention-for-improved","slug":"contextualised-graph-attention-for-improved","title":"Contextualised Graph Attention for Improved Relation Extraction","date":"2020-04-22","arxiv_id":"2004.10624","n_code_links":1,"syntology":null},{"paper":null,"slug":"mer-gcn-micro-expression-recognition-based-on","title":"MER-GCN: Micro Expression Recognition Based on Relation Modeling with Graph Convolutional Network","date":"2020-04-19","arxiv_id":"2004.08915","n_code_links":0,"syntology":null},{"paper":"/paper/vgcn-bert-augmenting-bert-with-graph","slug":"vgcn-bert-augmenting-bert-with-graph","title":"VGCN-BERT: Augmenting BERT with Graph Embedding for Text Classification","date":"2020-04-12","arxiv_id":"2004.05707","n_code_links":1,"syntology":{"ran":7,"of":9,"n_ran_checked":5,"n_instrument":2,"unverified":2,"pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["Louis-udm/VGCN-BERT"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/graph-highway-networks","slug":"graph-highway-networks","title":"Graph Highway Networks","date":"2020-04-09","arxiv_id":"2004.04635","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-graph-convolutional-network-composition","title":"A Graph Convolutional Network Composition Framework for Semi-supervised Classification","date":"2020-04-08","arxiv_id":"2004.03994","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-long-distance-relation-extraction","slug":"efficient-long-distance-relation-extraction","title":"Efficient long-distance relation extraction with DG-SpanBERT","date":"2020-04-07","arxiv_id":"2004.03636","n_code_links":0,"syntology":null},{"paper":null,"slug":"pooling-in-graph-convolutional-neural","title":"Pooling in Graph Convolutional Neural Networks","date":"2020-04-07","arxiv_id":"2004.03519","n_code_links":0,"syntology":null},{"paper":null,"slug":"attribute2vec-deep-network-embedding-through","title":"Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN","date":"2020-04-03","arxiv_id":"2004.01375","n_code_links":0,"syntology":null},{"paper":null,"slug":"feedergan-synthetic-feeder-generation-via","title":"FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets","date":"2020-04-03","arxiv_id":"2004.01407","n_code_links":0,"syntology":null},{"paper":null,"slug":"semi-supervised-cervical-dysplasia","title":"Semi-Supervised Cervical Dysplasia Classification With Learnable Graph Convolutional Network","date":"2020-04-01","arxiv_id":"2004.00191","n_code_links":0,"syntology":null},{"paper":"/paper/l-2-gcn-layer-wise-and-learned-efficient","slug":"l-2-gcn-layer-wise-and-learned-efficient","title":"L^2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks","date":"2020-03-30","arxiv_id":"2003.13606","n_code_links":2,"syntology":null},{"paper":null,"slug":"revisiting-over-smoothing-in-deep-gcns","title":"Revisiting Over-smoothing in Deep GCNs","date":"2020-03-30","arxiv_id":"2003.13663","n_code_links":0,"syntology":null},{"paper":null,"slug":"latent-patient-network-learning-for-automatic","title":"Latent-Graph Learning for Disease Prediction","date":"2020-03-27","arxiv_id":"2003.13620","n_code_links":0,"syntology":null},{"paper":"/paper/distillating-knowledge-from-graph","slug":"distillating-knowledge-from-graph","title":"Distilling Knowledge from Graph Convolutional Networks","date":"2020-03-23","arxiv_id":"2003.10477","n_code_links":1,"syntology":{"ran":7,"of":7,"n_ran_checked":7,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ihollywhy/DistillGCN.PyTorch"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/peeking-into-occluded-joints-a-novel","slug":"peeking-into-occluded-joints-a-novel","title":"Peeking into occluded joints: A novel framework for crowd pose estimation","date":"2020-03-23","arxiv_id":"2003.10506","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-flexible-framework-for-large-graph-learning","title":"An Uncoupled Training Architecture for Large Graph Learning","date":"2020-03-21","arxiv_id":"2003.09638","n_code_links":0,"syntology":null},{"paper":null,"slug":"cpr-gcn-conditional-partial-residual-graph","title":"CPR-GCN: Conditional Partial-Residual Graph Convolutional Network in Automated Anatomical Labeling of Coronary Arteries","date":"2020-03-19","arxiv_id":"2003.08560","n_code_links":0,"syntology":null},{"paper":"/paper/temporal-extension-module-for-skeleton-based-1","slug":"temporal-extension-module-for-skeleton-based-1","title":"Temporal Extension Module for Skeleton-Based Action Recognition","date":"2020-03-19","arxiv_id":"2003.08951","n_code_links":0,"syntology":null},{"paper":"/paper/scattering-gcn-overcoming-oversmoothness-in","slug":"scattering-gcn-overcoming-oversmoothness-in","title":"Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks","date":"2020-03-18","arxiv_id":"2003.08414","n_code_links":1,"syntology":null},{"paper":null,"slug":"relatext-exploiting-visual-relationships-for","title":"ReLaText: Exploiting Visual Relationships for Arbitrary-Shaped Scene Text Detection with Graph Convolutional Networks","date":"2020-03-16","arxiv_id":"2003.06999","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-logical-generalization-in-graph","slug":"evaluating-logical-generalization-in-graph","title":"Evaluating Logical Generalization in Graph Neural Networks","date":"2020-03-14","arxiv_id":"2003.06560","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-graph-embedding-with-limited-labeled","title":"Learning by Sampling and Compressing: Efficient Graph Representation Learning with Extremely Limited Annotations","date":"2020-03-13","arxiv_id":"2003.06100","n_code_links":0,"syntology":null},{"paper":null,"slug":"cross-gcn-enhancing-graph-convolutional","title":"Cross-GCN: Enhancing Graph Convolutional Network with $k$-Order Feature Interactions","date":"2020-03-05","arxiv_id":"2003.02587","n_code_links":0,"syntology":null}],"record_sha256":"98c30548bf8cfce21b41c1f7811c6e46eb9ab53fb20a10e6a0d46f8c6c3938d7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}