{"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-convolutional-networks/papers/4","list_of":"/method/graph-convolutional-networks","method":"Graph Convolutional Networks","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":4,"pages_in_order":4,"rows_per_page":100,"rows":[301,388],"of":388,"counts":{"archive_papers_tagged":388,"with_a_code_link":178,"where_syntology_ran_a_sample":56,"not_listed_spam_title":0,"listed":388,"listed_where_code_ran":56,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":48,"every_run_a_failure_of_syntologys_instrument":8,"listed_with_a_run_with_no_instrument_failure":48,"listed_every_run_a_failure_of_syntologys_instrument":8,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/graph-convolutional-networks","prev":"/method/graph-convolutional-networks/papers/3","next":null,"papers":[{"paper":null,"slug":"histographs-graphs-in-histopathology","title":"Histographs: Graphs in Histopathology","date":"2019-08-14","arxiv_id":"1908.05020","n_code_links":0,"syntology":null},{"paper":"/paper/graphsw-a-training-protocol-based-on-stage","slug":"graphsw-a-training-protocol-based-on-stage","title":"GraphSW: a training protocol based on stage-wise training for GNN-based Recommender Model","date":"2019-08-13","arxiv_id":"1908.05611","n_code_links":1,"syntology":null},{"paper":"/paper/aligning-linguistic-words-and-visual-semantic","slug":"aligning-linguistic-words-and-visual-semantic","title":"Aligning Linguistic Words and Visual Semantic Units for Image Captioning","date":"2019-08-06","arxiv_id":"1908.02127","n_code_links":1,"syntology":null},{"paper":null,"slug":"od-gcn-object-detection-by-knowledge-graph","title":"OD-GCN: Object Detection Boosted by Knowledge GCN","date":"2019-08-06","arxiv_id":"1908.04385","n_code_links":0,"syntology":null},{"paper":null,"slug":"hybrid-low-order-and-higher-order-graph","title":"Hybrid Low-order and Higher-order Graph Convolutional Networks","date":"2019-08-02","arxiv_id":"1908.00673","n_code_links":0,"syntology":null},{"paper":"/paper/anti-money-laundering-in-bitcoin","slug":"anti-money-laundering-in-bitcoin","title":"Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics","date":"2019-07-31","arxiv_id":"1908.02591","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 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) · 0 unverified","official":null}},{"paper":"/paper/the-truly-deep-graph-convolutional-networks","slug":"the-truly-deep-graph-convolutional-networks","title":"DropEdge: Towards Deep Graph Convolutional Networks on Node Classification","date":"2019-07-25","arxiv_id":"1907.10903","n_code_links":7,"syntology":{"ran":4,"of":5,"n_ran_checked":4,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["DropEdge/DropEdge"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/spatiotemporal-graph-routing-for-skeleton","slug":"spatiotemporal-graph-routing-for-skeleton","title":"Spatiotemporal graph routing for skeleton-based action recognition","date":"2019-07-17","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/understanding-the-representation-power-of","slug":"understanding-the-representation-power-of","title":"Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology","date":"2019-07-11","arxiv_id":"1907.05008","n_code_links":1,"syntology":null},{"paper":null,"slug":"label-aware-graph-convolutional-network-not","title":"Label-Aware Graph Convolutional Networks","date":"2019-07-10","arxiv_id":"1907.04707","n_code_links":0,"syntology":null},{"paper":"/paper/non-local-graph-convolutional-networks-for-1","slug":"non-local-graph-convolutional-networks-for-1","title":"Non-Local Graph Convolutional Networks for Skeleton-Based Action Recognition","date":"2019-07-04","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/graphdta-prediction-of-drugtarget-binding","slug":"graphdta-prediction-of-drugtarget-binding","title":"GraphDTA: prediction of drug–target binding affinity using graph convolutional networks","date":"2019-07-02","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/structure-fusion-based-on-graph-convolutional","slug":"structure-fusion-based-on-graph-convolutional","title":"Structure fusion based on graph convolutional networks for semi-supervised classification","date":"2019-07-02","arxiv_id":"1907.02586","n_code_links":0,"syntology":null},{"paper":null,"slug":"encoding-social-information-with-graph","title":"Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media","date":"2019-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/graphrel-modeling-text-as-relational-graphs","slug":"graphrel-modeling-text-as-relational-graphs","title":"GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction","date":"2019-07-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"prediction-of-small-molecule-kinase","title":"Prediction of Small Molecule Kinase Inhibitors for Chemotherapy Using Deep Learning","date":"2019-06-30","arxiv_id":"1907.00329","n_code_links":0,"syntology":null},{"paper":"/paper/certifiable-robustness-and-robust-training","slug":"certifiable-robustness-and-robust-training","title":"Certifiable Robustness and Robust Training for Graph Convolutional Networks","date":"2019-06-28","arxiv_id":"1906.12269","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":0,"phrase":"3 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; 3 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/attention-guided-graph-convolutional-networks","slug":"attention-guided-graph-convolutional-networks","title":"Attention Guided Graph Convolutional Networks for Relation Extraction","date":"2019-06-18","arxiv_id":"1906.07510","n_code_links":2,"syntology":{"ran":13,"of":13,"n_ran_checked":12,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["Cartus/AGGCN_TACRED"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/modeling-sentiment-dependencies-with-graph","slug":"modeling-sentiment-dependencies-with-graph","title":"Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment Classification","date":"2019-06-11","arxiv_id":"1906.04501","n_code_links":1,"syntology":null},{"paper":"/paper/break-the-ceiling-stronger-multi-scale-deep","slug":"break-the-ceiling-stronger-multi-scale-deep","title":"Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks","date":"2019-06-05","arxiv_id":"1906.02174","n_code_links":1,"syntology":{"ran":5,"of":9,"n_ran_checked":5,"n_instrument":0,"unverified":4,"pointer_only":0,"phrase":"5 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; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["PwnerHarry/Stronger_GCN"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/variational-spectral-graph-convolutional","slug":"variational-spectral-graph-convolutional","title":"Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings","date":"2019-06-05","arxiv_id":"1906.01852","n_code_links":1,"syntology":null},{"paper":"/paper/an-efficient-graph-convolutional-network","slug":"an-efficient-graph-convolutional-network","title":"An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem","date":"2019-06-04","arxiv_id":"1906.01227","n_code_links":4,"syntology":{"ran":7,"of":9,"n_ran_checked":6,"n_instrument":1,"unverified":2,"pointer_only":0,"phrase":"7 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; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["chaitjo/graph-convnet-tsp"],"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":["listed","official"]}}},{"paper":"/paper/attributed-graph-clustering-via-adaptive","slug":"attributed-graph-clustering-via-adaptive","title":"Attributed Graph Clustering via Adaptive Graph Convolution","date":"2019-06-04","arxiv_id":"1906.01210","n_code_links":1,"syntology":null},{"paper":null,"slug":"exploiting-edge-features-for-graph-neural","title":"Exploiting Edge Features for Graph Neural Networks","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/graph-convolutional-tracking","slug":"graph-convolutional-tracking","title":"Graph Convolutional Tracking","date":"2019-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/quantifying-the-alignment-of-graph-and","slug":"quantifying-the-alignment-of-graph-and","title":"Quantifying the Alignment of Graph and Features in Deep Learning","date":"2019-05-30","arxiv_id":"1905.12921","n_code_links":1,"syntology":{"ran":6,"of":7,"n_ran_checked":6,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":{"repos":["haczqyf/gcn-data-alignment"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"star-gcn-stacked-and-reconstructed-graph","title":"STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems","date":"2019-05-27","arxiv_id":"1905.13129","n_code_links":0,"syntology":null},{"paper":"/paper/simulation-and-augmentation-of-social","slug":"simulation-and-augmentation-of-social","title":"Simulation and Augmentation of Social Networks for Building Deep Learning Models","date":"2019-05-22","arxiv_id":"1905.09087","n_code_links":1,"syntology":null},{"paper":"/paper/joint-embedding-of-structure-and-features-via","slug":"joint-embedding-of-structure-and-features-via","title":"Joint embedding of structure and features via graph convolutional networks","date":"2019-05-21","arxiv_id":"1905.08636","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-reinforcement-learning-based-channel","title":"Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks","date":"2019-05-17","arxiv_id":"1905.07144","n_code_links":0,"syntology":null},{"paper":"/paper/ncrna-classification-with-graph-convolutional","slug":"ncrna-classification-with-graph-convolutional","title":"ncRNA Classification with Graph Convolutional Networks","date":"2019-05-16","arxiv_id":"1905.06515","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":null,"slug":"190503743","title":"Interactive Image Generation Using Scene Graphs","date":"2019-05-09","arxiv_id":"1905.03743","n_code_links":0,"syntology":null},{"paper":"/paper/dynamic-prediction-of-origin-destination","slug":"dynamic-prediction-of-origin-destination","title":"Dynamic Origin-Destination Matrix Prediction with Line Graph Neural Networks and Kalman Filter","date":"2019-05-01","arxiv_id":"1905.00406","n_code_links":1,"syntology":null},{"paper":null,"slug":"few-shot-classification-on-graphs-with","title":"Few-shot Classification on Graphs with Structural Regularized GCNs","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"graph-convolutional-network-with-sequential","title":"Graph Convolutional Network with Sequential Attention For Goal-Oriented Dialogue Systems","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"link-prediction-in-hypergraphs-using-graph","title":"Link Prediction in Hypergraphs using Graph Convolutional Networks","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/relational-graph-attention-networks-1","slug":"relational-graph-attention-networks-1","title":"Relational Graph Attention Networks","date":"2019-04-11","arxiv_id":"1904.05811","n_code_links":2,"syntology":{"ran":7,"of":10,"n_ran_checked":7,"n_instrument":0,"unverified":3,"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) · 3 unverified","official":{"repos":["Babylonpartners/rgat"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/can-gcns-go-as-deep-as-cnns","slug":"can-gcns-go-as-deep-as-cnns","title":"DeepGCNs: Can GCNs Go as Deep as CNNs?","date":"2019-04-07","arxiv_id":"1904.03751","n_code_links":1,"syntology":null},{"paper":"/paper/semantic-graph-convolutional-networks-for-3d","slug":"semantic-graph-convolutional-networks-for-3d","title":"Semantic Graph Convolutional Networks for 3D Human Pose Regression","date":"2019-04-06","arxiv_id":"1904.03345","n_code_links":5,"syntology":{"ran":3,"of":8,"n_ran_checked":3,"n_instrument":0,"unverified":5,"pointer_only":5,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","official":{"repos":["garyzhao/SemGCN"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"abusive-language-detection-with-graph","title":"Abusive Language Detection with Graph Convolutional Networks","date":"2019-04-05","arxiv_id":"1904.04073","n_code_links":0,"syntology":null},{"paper":"/paper/dagcn-dual-attention-graph-convolutional","slug":"dagcn-dual-attention-graph-convolutional","title":"DAGCN: Dual Attention Graph Convolutional Networks","date":"2019-04-04","arxiv_id":"1904.02278","n_code_links":1,"syntology":null},{"paper":"/paper/learning-discrete-structures-for-graph-neural","slug":"learning-discrete-structures-for-graph-neural","title":"Learning Discrete Structures for Graph Neural Networks","date":"2019-03-28","arxiv_id":"1903.11960","n_code_links":2,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":2,"phrase":"0 ran · 2 unverified","official":{"repos":["lucfra/LDS","lucfra/LDS-GNN"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":"/paper/190412575","slug":"190412575","title":"Knowledge Graph Convolutional Networks for Recommender Systems","date":"2019-03-18","arxiv_id":"1904.12575","n_code_links":8,"syntology":{"ran":3,"of":5,"n_ran_checked":2,"n_instrument":1,"unverified":2,"pointer_only":1,"phrase":"3 ran (of which 0 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":["hwwang55/KGCN"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/fisher-bures-adversary-graph-convolutional","slug":"fisher-bures-adversary-graph-convolutional","title":"Fisher-Bures Adversary Graph Convolutional Networks","date":"2019-03-11","arxiv_id":"1903.04154","n_code_links":1,"syntology":{"ran":6,"of":8,"n_ran_checked":6,"n_instrument":0,"unverified":2,"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) · 2 unverified","official":{"repos":["stellargraph/FisherGCN"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/the-vulnerabilities-of-graph-convolutional","slug":"the-vulnerabilities-of-graph-convolutional","title":"Adversarial Examples on Graph Data: Deep Insights into Attack and Defense","date":"2019-03-05","arxiv_id":"1903.01610","n_code_links":2,"syntology":{"ran":10,"of":12,"n_ran_checked":10,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["stellargraph/stellargraph"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/3d-graph-convolutional-networks-with-temporal","slug":"3d-graph-convolutional-networks-with-temporal","title":"3D Graph Convolutional Networks with Temporal Graphs: A Spatial Information Free Framework For Traffic Forecasting","date":"2019-03-03","arxiv_id":"1903.00919","n_code_links":0,"syntology":null},{"paper":null,"slug":"virtual-adversarial-training-on-graph","title":"Virtual Adversarial Training on Graph Convolutional Networks in Node Classification","date":"2019-02-28","arxiv_id":"1902.11045","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-to-generate-questions-by-learning","title":"Learning to Generate Questions by Learning What not to Generate","date":"2019-02-27","arxiv_id":"1902.10418","n_code_links":0,"syntology":null},{"paper":null,"slug":"batch-virtual-adversarial-training-for-graph","title":"Batch Virtual Adversarial Training for Graph Convolutional Networks","date":"2019-02-25","arxiv_id":"1902.09192","n_code_links":0,"syntology":null},{"paper":null,"slug":"constant-time-graph-neural-networks","title":"Constant Time Graph Neural Networks","date":"2019-01-23","arxiv_id":"1901.07868","n_code_links":0,"syntology":null},{"paper":null,"slug":"multiple-graph-adversarial-learning","title":"Multiple Graph Adversarial Learning","date":"2019-01-22","arxiv_id":"1901.07439","n_code_links":0,"syntology":null},{"paper":"/paper/learning-graph-pooling-and-hybrid","slug":"learning-graph-pooling-and-hybrid","title":"Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations","date":"2019-01-21","arxiv_id":"1901.06965","n_code_links":1,"syntology":null},{"paper":"/paper/skeleton-based-action-recognition-of-people","slug":"skeleton-based-action-recognition-of-people","title":"Skeleton-based Action Recognition of People Handling Objects","date":"2019-01-21","arxiv_id":"1901.06882","n_code_links":0,"syntology":null},{"paper":null,"slug":"stacked-spatio-temporal-graph-convolutional","title":"Stacked Spatio-Temporal Graph Convolutional Networks for Action Segmentation","date":"2018-11-26","arxiv_id":"1811.10575","n_code_links":0,"syntology":null},{"paper":"/paper/on-filter-size-in-graph-convolutional","slug":"on-filter-size-in-graph-convolutional","title":"On Filter Size in Graph Convolutional Networks","date":"2018-11-23","arxiv_id":"1811.10435","n_code_links":1,"syntology":null},{"paper":"/paper/spectral-multigraph-networks-for-discovering","slug":"spectral-multigraph-networks-for-discovering","title":"Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules","date":"2018-11-23","arxiv_id":"1811.09595","n_code_links":1,"syntology":null},{"paper":null,"slug":"textbook-question-answering-with-knowledge","title":"Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension","date":"2018-11-01","arxiv_id":"1811.00232","n_code_links":0,"syntology":null},{"paper":null,"slug":"attack-graph-convolutional-networks-by-adding","title":"Attack Graph Convolutional Networks by Adding Fake Nodes","date":"2018-10-25","arxiv_id":"1810.10751","n_code_links":0,"syntology":null},{"paper":"/paper/deep-graph-convolutional-encoders-for","slug":"deep-graph-convolutional-encoders-for","title":"Deep Graph Convolutional Encoders for Structured Data to Text Generation","date":"2018-10-23","arxiv_id":"1810.09995","n_code_links":2,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"3 ran (of which 0 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) · 0 unverified","official":{"repos":["diegma/graph-2-text"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"finding-appropriate-traffic-regulations-via","title":"Finding Appropriate Traffic Regulations via Graph Convolutional Networks","date":"2018-10-23","arxiv_id":"1810.09712","n_code_links":0,"syntology":null},{"paper":"/paper/visual-semantic-navigation-using-scene-priors","slug":"visual-semantic-navigation-using-scene-priors","title":"Visual Semantic Navigation using Scene Priors","date":"2018-10-15","arxiv_id":"1810.06543","n_code_links":1,"syntology":null},{"paper":"/paper/predict-then-propagate-graph-neural-networks","slug":"predict-then-propagate-graph-neural-networks","title":"Predict then Propagate: Graph Neural Networks meet Personalized PageRank","date":"2018-10-14","arxiv_id":"1810.05997","n_code_links":5,"syntology":{"ran":11,"of":12,"n_ran_checked":10,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["klicperajo/ppnp"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/cross-lingual-knowledge-graph-alignment-via","slug":"cross-lingual-knowledge-graph-alignment-via","title":"Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks","date":"2018-10-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/graphbtm-graph-enhanced-autoencoded","slug":"graphbtm-graph-enhanced-autoencoded","title":"GraphBTM: Graph Enhanced Autoencoded Variational Inference for Biterm Topic Model","date":"2018-10-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/how-powerful-are-graph-neural-networks","slug":"how-powerful-are-graph-neural-networks","title":"How Powerful are Graph Neural Networks?","date":"2018-10-01","arxiv_id":"1810.00826","n_code_links":19,"syntology":{"ran":6,"of":10,"n_ran_checked":6,"n_instrument":0,"unverified":4,"pointer_only":5,"phrase":"6 ran (of which 2 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) · 4 unverified","official":{"repos":["weihua916/powerful-gnns"],"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":["listed","official"]}}},{"paper":"/paper/hypergraph-neural-networks","slug":"hypergraph-neural-networks","title":"Hypergraph Neural Networks","date":"2018-09-25","arxiv_id":"1809.09401","n_code_links":4,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"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) · 1 unverified","official":null}},{"paper":null,"slug":"higher-order-graph-convolutional-networks","title":"Higher-order Graph Convolutional Networks","date":"2018-09-12","arxiv_id":"1809.07697","n_code_links":0,"syntology":null},{"paper":null,"slug":"non-parametric-variational-inference-with","title":"Non-Parametric Variational Inference with Graph Convolutional Networks for Gaussian Processes","date":"2018-09-08","arxiv_id":"1809.02838","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploiting-edge-features-in-graph-neural","title":"Exploiting Edge Features in Graph Neural Networks","date":"2018-09-07","arxiv_id":"1809.02709","n_code_links":0,"syntology":null},{"paper":null,"slug":"compositional-learning-for-human-object","title":"Compositional Learning for Human Object Interaction","date":"2018-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/question-answering-by-reasoning-across","slug":"question-answering-by-reasoning-across","title":"Question Answering by Reasoning Across Documents with Graph Convolutional Networks","date":"2018-08-29","arxiv_id":"1808.09920","n_code_links":1,"syntology":null},{"paper":"/paper/bayesgrad-explaining-predictions-of-graph","slug":"bayesgrad-explaining-predictions-of-graph","title":"BayesGrad: Explaining Predictions of Graph Convolutional Networks","date":"2018-07-04","arxiv_id":"1807.01985","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":4,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["pfnet-research/bayesgrad"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/representation-learning-on-graphs-with","slug":"representation-learning-on-graphs-with","title":"Representation Learning on Graphs with Jumping Knowledge Networks","date":"2018-06-09","arxiv_id":"1806.03536","n_code_links":5,"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":null}},{"paper":"/paper/disease-prediction-using-graph-convolutional","slug":"disease-prediction-using-graph-convolutional","title":"Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease","date":"2018-06-05","arxiv_id":"1806.01738","n_code_links":1,"syntology":null},{"paper":"/paper/videos-as-space-time-region-graphs","slug":"videos-as-space-time-region-graphs","title":"Videos as Space-Time Region Graphs","date":"2018-06-05","arxiv_id":"1806.01810","n_code_links":0,"syntology":null},{"paper":"/paper/multi-view-graph-convolutional-network-and","slug":"multi-view-graph-convolutional-network-and","title":"Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease","date":"2018-05-22","arxiv_id":"1805.08801","n_code_links":1,"syntology":null},{"paper":"/paper/non-local-graph-convolutional-networks-for","slug":"non-local-graph-convolutional-networks-for","title":"Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition","date":"2018-05-20","arxiv_id":"1805.07694","n_code_links":4,"syntology":null},{"paper":"/paper/enhancing-drug-drug-interaction-extraction","slug":"enhancing-drug-drug-interaction-extraction","title":"Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information","date":"2018-05-15","arxiv_id":"1805.05593","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-layered-parallel-graph-convolutional","title":"Multi Layered-Parallel Graph Convolutional Network (ML-PGCN) for Disease Prediction","date":"2018-04-28","arxiv_id":"1804.10776","n_code_links":0,"syntology":null},{"paper":"/paper/exploiting-semantics-in-neural-machine","slug":"exploiting-semantics-in-neural-machine","title":"Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks","date":"2018-04-23","arxiv_id":"1804.08313","n_code_links":0,"syntology":null},{"paper":"/paper/semi-supervised-user-geolocation-via-graph","slug":"semi-supervised-user-geolocation-via-graph","title":"Semi-supervised User Geolocation via Graph Convolutional Networks","date":"2018-04-22","arxiv_id":"1804.08049","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 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/general-purpose-deep-point-cloud-feature","slug":"general-purpose-deep-point-cloud-feature","title":"General-Purpose Deep Point Cloud Feature Extractor","date":"2018-03-12","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-graph-convolutional-networks-with","title":"Improving Graph Convolutional Networks with Non-Parametric Activation Functions","date":"2018-02-26","arxiv_id":"1802.09405","n_code_links":0,"syntology":null},{"paper":"/paper/fastgcn-fast-learning-with-graph","slug":"fastgcn-fast-learning-with-graph","title":"FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling","date":"2018-01-30","arxiv_id":"1801.10247","n_code_links":4,"syntology":{"ran":0,"of":3,"n_ran_checked":0,"n_instrument":0,"unverified":3,"pointer_only":3,"phrase":"0 ran · 3 unverified","official":{"repos":["matenure/FastGCN"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"paper":"/paper/deeper-insights-into-graph-convolutional","slug":"deeper-insights-into-graph-convolutional","title":"Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning","date":"2018-01-22","arxiv_id":"1801.07606","n_code_links":1,"syntology":null},{"paper":null,"slug":"automatically-inferring-data-quality-for","title":"Automatically Inferring Data Quality for Spatiotemporal Forecasting","date":"2018-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/encoding-sentences-with-graph-convolutional","slug":"encoding-sentences-with-graph-convolutional","title":"Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling","date":"2017-03-14","arxiv_id":"1703.04826","n_code_links":2,"syntology":{"ran":4,"of":7,"n_ran_checked":4,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["diegma/neural-dep-srl"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/semi-supervised-classification-with-graph","slug":"semi-supervised-classification-with-graph","title":"Semi-Supervised Classification with Graph Convolutional Networks","date":"2016-09-09","arxiv_id":"1609.02907","n_code_links":55,"syntology":{"ran":39,"of":58,"n_ran_checked":34,"n_instrument":5,"unverified":19,"pointer_only":23,"phrase":"39 ran (of which 13 constructed an object rather than computing a result; 34 with no instrument failure: 0 honoured, 1 violated, 33 with no contract checked; 5 where Syntology's instrument failed) · 19 unverified","official":{"repos":["tkipf/pygcn"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}}],"record_sha256":"1957c7a0880977f48be6d367ec4ad1bea96ad1fe36974a525c976a74372a2a47","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}