{"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/5","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":5,"pages_in_order":10,"rows_per_page":100,"rows":[401,500],"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/4","next":"/method/gcn/papers/6","papers":[{"paper":null,"slug":"degree-aware-based-adversarial-graph","title":"Degree aware based adversarial graph convolutional networks for entity alignment in heterogeneous knowledge graph","date":"2022-04-28","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/gcn-ffnn-a-two-stream-deep-model-for-learning","slug":"gcn-ffnn-a-two-stream-deep-model-for-learning","title":"GCN-FFNN: A Two-Stream Deep Model for Learning Solution to Partial Differential Equations","date":"2022-04-28","arxiv_id":"2204.13744","n_code_links":1,"syntology":null},{"paper":null,"slug":"interpretable-graph-convolutional-network-of","title":"Interpretable Graph Convolutional Network of Multi-Modality Brain Imaging for Alzheimer's Disease Diagnosis","date":"2022-04-27","arxiv_id":"2204.13188","n_code_links":0,"syntology":null},{"paper":null,"slug":"unified-gcns-towards-connecting-gcns-with","title":"Unified GCNs: Towards Connecting GCNs with CNNs","date":"2022-04-26","arxiv_id":"2204.12300","n_code_links":0,"syntology":null},{"paper":"/paper/less-is-more-reweighting-important-spectral","slug":"less-is-more-reweighting-important-spectral","title":"Less is More: Reweighting Important Spectral Graph Features for Recommendation","date":"2022-04-24","arxiv_id":"2204.11346","n_code_links":1,"syntology":null},{"paper":"/paper/remote-sensing-cross-modal-text-image","slug":"remote-sensing-cross-modal-text-image","title":"Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information","date":"2022-04-21","arxiv_id":"2204.09860","n_code_links":1,"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":{"repos":["xiaoyuan1996/galr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"adaptive-cross-attention-driven-spatial","title":"Adaptive Cross-Attention-Driven Spatial-Spectral Graph Convolutional Network for Hyperspectral Image Classification","date":"2022-04-12","arxiv_id":"2204.05823","n_code_links":0,"syntology":null},{"paper":"/paper/ia-gcn-interactive-graph-convolutional","slug":"ia-gcn-interactive-graph-convolutional","title":"IA-GCN: Interactive Graph Convolutional Network for Recommendation","date":"2022-04-08","arxiv_id":"2204.03827","n_code_links":1,"syntology":null},{"paper":"/paper/accelerating-backward-aggregation-in-gcn","slug":"accelerating-backward-aggregation-in-gcn","title":"Accelerating Backward Aggregation in GCN Training with Execution Path Preparing on GPUs","date":"2022-04-06","arxiv_id":"2204.02662","n_code_links":1,"syntology":null},{"paper":"/paper/novel-solubility-prediction-models-molecular","slug":"novel-solubility-prediction-models-molecular","title":"Novel Solubility Prediction Models: Molecular Fingerprints and Physicochemical Features vs Graph Convolutional Neural Networks","date":"2022-04-04","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-reinforcement-learning-with-graph","title":"Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms","date":"2022-03-31","arxiv_id":"2203.16732","n_code_links":0,"syntology":null},{"paper":null,"slug":"spatiotemporal-focus-for-skeleton-based","title":"SpatioTemporal Focus for Skeleton-based Action Recognition","date":"2022-03-31","arxiv_id":"2203.16767","n_code_links":0,"syntology":null},{"paper":null,"slug":"neighbor-enhanced-graph-convolutional","title":"Neighbor Enhanced Graph Convolutional Networks for Node Classification and Recommendation","date":"2022-03-30","arxiv_id":"2203.16097","n_code_links":0,"syntology":null},{"paper":"/paper/understanding-graph-convolutional-networks","slug":"understanding-graph-convolutional-networks","title":"Understanding Graph Convolutional Networks for Text Classification","date":"2022-03-30","arxiv_id":"2203.16060","n_code_links":1,"syntology":null},{"paper":"/paper/distributed-link-sparsification-for-scalable","slug":"distributed-link-sparsification-for-scalable","title":"Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks","date":"2022-03-27","arxiv_id":"2203.14339","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-question-answering-over-knowledge","title":"Improving Question Answering over Knowledge Graphs Using Graph Summarization","date":"2022-03-25","arxiv_id":"2203.13570","n_code_links":0,"syntology":null},{"paper":null,"slug":"lightweight-graph-convolutional-networks-with","title":"Lightweight Graph Convolutional Networks with Topologically Consistent Magnitude Pruning","date":"2022-03-25","arxiv_id":"2203.13616","n_code_links":0,"syntology":null},{"paper":"/paper/semisupervised-cross-scale-graph-prototypical","slug":"semisupervised-cross-scale-graph-prototypical","title":"Semisupervised Cross-scale Graph Prototypical Network for Hyperspectral Image Classification","date":"2022-03-23","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/bns-gcn-efficient-full-graph-training-of","slug":"bns-gcn-efficient-full-graph-training-of","title":"BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling","date":"2022-03-21","arxiv_id":"2203.10983","n_code_links":2,"syntology":null},{"paper":"/paper/3d-human-pose-estimation-using-mobius-graph","slug":"3d-human-pose-estimation-using-mobius-graph","title":"3D Human Pose Estimation Using Möbius Graph Convolutional Networks","date":"2022-03-20","arxiv_id":"2203.10554","n_code_links":0,"syntology":null},{"paper":"/paper/pipegcn-efficient-full-graph-training-of-1","slug":"pipegcn-efficient-full-graph-training-of-1","title":"PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication","date":"2022-03-20","arxiv_id":"2203.10428","n_code_links":1,"syntology":{"ran":1,"of":4,"n_ran_checked":1,"n_instrument":0,"unverified":3,"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) · 3 unverified","official":{"repos":["RICE-EIC/PipeGCN"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/exploiting-neighbor-effect-conv-agnostic-gnns","slug":"exploiting-neighbor-effect-conv-agnostic-gnns","title":"Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily","date":"2022-03-19","arxiv_id":"2203.11200","n_code_links":1,"syntology":null},{"paper":"/paper/gate-graph-cca-for-temporal-self-supervised","slug":"gate-graph-cca-for-temporal-self-supervised","title":"GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysis","date":"2022-03-17","arxiv_id":"2203.09034","n_code_links":1,"syntology":null},{"paper":"/paper/interacting-attention-graph-for-single-image","slug":"interacting-attention-graph-for-single-image","title":"Interacting Attention Graph for Single Image Two-Hand Reconstruction","date":"2022-03-17","arxiv_id":"2203.09364","n_code_links":1,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"pointer_only":4,"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) · 2 unverified","official":{"repos":["dw1010/intaghand"],"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":["official"]}}},{"paper":null,"slug":"graph-neural-network-sensitivity-under","title":"Graph Convolutional Neural Networks Sensitivity under Probabilistic Error Model","date":"2022-03-15","arxiv_id":"2203.07831","n_code_links":0,"syntology":null},{"paper":null,"slug":"i-gcn-a-graph-convolutional-network","title":"I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization","date":"2022-03-07","arxiv_id":"2203.03606","n_code_links":0,"syntology":null},{"paper":null,"slug":"ontological-learning-from-weak-labels","title":"Ontological Learning from Weak Labels","date":"2022-03-04","arxiv_id":"2203.02483","n_code_links":0,"syntology":null},{"paper":null,"slug":"pay-attention-to-relations-multi-embeddings","title":"Pay Attention to Relations: Multi-embeddings for Attributed Multiplex Networks","date":"2022-03-03","arxiv_id":"2203.01903","n_code_links":0,"syntology":null},{"paper":null,"slug":"spatial-temporal-gating-adjacency-gcn-for","title":"Spatio-Temporal Gating-Adjacency GCN for Human Motion Prediction","date":"2022-03-03","arxiv_id":"2203.01474","n_code_links":0,"syntology":null},{"paper":null,"slug":"grow-a-row-stationary-sparse-dense-gemm","title":"GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networks","date":"2022-03-01","arxiv_id":"2203.00158","n_code_links":0,"syntology":null},{"paper":"/paper/region-or-global-a-principle-for-negative","slug":"region-or-global-a-principle-for-negative","title":"Region or Global? A Principle for Negative Sampling in Graph-based Recommendation","date":"2022-03-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"esw-edge-weights-ensemble-stochastic","title":"ESW Edge-Weights : Ensemble Stochastic Watershed Edge-Weights for Hyperspectral Image Classification","date":"2022-02-28","arxiv_id":"2202.13502","n_code_links":0,"syntology":null},{"paper":null,"slug":"rawlsgcn-towards-rawlsian-difference","title":"RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network","date":"2022-02-28","arxiv_id":"2202.13547","n_code_links":0,"syntology":null},{"paper":null,"slug":"sparse-graph-learning-with-eigen-gap-for","title":"Sparse Graph Learning with Spectrum Prior for Deep Graph Convolutional Networks","date":"2022-02-28","arxiv_id":"2202.13526","n_code_links":0,"syntology":null},{"paper":null,"slug":"gcn-transformer-for-short-term-passenger-flow","title":"Spatial-Temporal Attention Fusion Network for short-term passenger flow prediction on holidays in urban rail transit systems","date":"2022-02-27","arxiv_id":"2203.00007","n_code_links":0,"syntology":null},{"paper":null,"slug":"classification-of-computer-aided-engineering","title":"Classification of Computer Aided Engineering (CAE) Parts Using Graph Convolutional Networks","date":"2022-02-23","arxiv_id":"2202.11289","n_code_links":0,"syntology":null},{"paper":null,"slug":"path-aware-graph-attention-for-hd-maps-in","title":"Path-Aware Graph Attention for HD Maps in Motion Prediction","date":"2022-02-23","arxiv_id":"2202.13772","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-two-branch-neural-network-for-gait","title":"Combining the Silhouette and Skeleton Data for Gait Recognition","date":"2022-02-22","arxiv_id":"2202.10645","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-human-mobility-for-multi-pattern","title":"Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Framework","date":"2022-02-17","arxiv_id":"2202.10339","n_code_links":0,"syntology":null},{"paper":null,"slug":"cengcn-centralized-convolutional-networks","title":"CenGCN: Centralized Convolutional Networks with Vertex Imbalance for Scale-Free Graphs","date":"2022-02-16","arxiv_id":"2202.07826","n_code_links":0,"syntology":null},{"paper":"/paper/learning-to-solve-routing-problems-via","slug":"learning-to-solve-routing-problems-via","title":"Learning to Solve Routing Problems via Distributionally Robust Optimization","date":"2022-02-15","arxiv_id":"2202.07241","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":0,"n_instrument":2,"unverified":1,"pointer_only":3,"phrase":"2 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; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["jiang-yuan/Learning-routing-DRO"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"paper":"/paper/hyla-hyperbolic-laplacian-features-for-graph","slug":"hyla-hyperbolic-laplacian-features-for-graph","title":"Random Laplacian Features for Learning with Hyperbolic Space","date":"2022-02-14","arxiv_id":"2202.06854","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ydtydr/hyla"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"deep-graph-learning-for-spatially-varying","title":"Deep Graph Learning for Spatially-Varying Indoor Lighting Prediction","date":"2022-02-13","arxiv_id":"2202.06300","n_code_links":0,"syntology":null},{"paper":"/paper/convolutional-neural-networks-on-graphs-with-1","slug":"convolutional-neural-networks-on-graphs-with-1","title":"Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited","date":"2022-02-04","arxiv_id":"2202.03580","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":0,"n_instrument":1,"unverified":2,"pointer_only":3,"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":["ivam-he/chebnetii"],"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":"investigating-transfer-learning-in-graph","title":"Investigating Transfer Learning in Graph Neural Networks","date":"2022-02-01","arxiv_id":"2202.00740","n_code_links":0,"syntology":null},{"paper":"/paper/smgrl-a-scalable-multi-resolution-graph","slug":"smgrl-a-scalable-multi-resolution-graph","title":"SMGRL: Scalable Multi-resolution Graph Representation Learning","date":"2022-01-29","arxiv_id":"2201.12670","n_code_links":1,"syntology":null},{"paper":"/paper/fedgcn-convergence-and-communication","slug":"fedgcn-convergence-and-communication","title":"FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks","date":"2022-01-28","arxiv_id":"2201.12433","n_code_links":2,"syntology":{"ran":13,"of":19,"n_ran_checked":13,"n_instrument":0,"unverified":6,"pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","official":{"repos":["yh-yao/FedGCN"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"jointly-learning-knowledge-embedding-and","title":"Jointly Learning Knowledge Embedding and Neighborhood Consensus with Relational Knowledge Distillation for Entity Alignment","date":"2022-01-25","arxiv_id":"2201.11249","n_code_links":0,"syntology":null},{"paper":"/paper/graph-neural-diffusion-networks-for-semi","slug":"graph-neural-diffusion-networks-for-semi","title":"Graph Neural Diffusion Networks for Semi-supervised Learning","date":"2022-01-24","arxiv_id":"2201.09698","n_code_links":1,"syntology":null},{"paper":null,"slug":"overcoming-oversmoothness-in-graph","title":"Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks","date":"2022-01-22","arxiv_id":"2201.08932","n_code_links":0,"syntology":null},{"paper":null,"slug":"taxonomy-enrichment-with-text-and-graph","title":"Taxonomy Enrichment with Text and Graph Vector Representations","date":"2022-01-21","arxiv_id":"2201.08598","n_code_links":0,"syntology":null},{"paper":"/paper/structure-based-drug-drug-interaction","slug":"structure-based-drug-drug-interaction","title":"Structure-Based Drug-Drug Interaction Detection via Expressive Graph Convolutional Networks and Deep Sets","date":"2022-01-20","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/decoupling-the-depth-and-scope-of-graph-1","slug":"decoupling-the-depth-and-scope-of-graph-1","title":"Decoupling the Depth and Scope of Graph Neural Networks","date":"2022-01-19","arxiv_id":"2201.07858","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"pointer_only":2,"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) · 1 unverified","official":{"repos":["facebookresearch/shaDow_GNN"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/a-comparative-study-of-the-performance-for","slug":"a-comparative-study-of-the-performance-for","title":"A Comparative Study of the Performance for Predicting Biodegradability Classification: The Quantitative Structure–Activity Relationship Model vs the Graph Convolutional Network","date":"2022-01-14","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/contrastive-laplacian-eigenmaps-1","slug":"contrastive-laplacian-eigenmaps-1","title":"Contrastive Laplacian Eigenmaps","date":"2022-01-14","arxiv_id":"2201.05493","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":2,"phrase":"2 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; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["allenhaozhu/coles"],"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":["official"]}}},{"paper":null,"slug":"supervised-contrastive-learning-for-2","title":"Supervised Contrastive Learning for Recommendation","date":"2022-01-10","arxiv_id":"2201.03144","n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-based-recommendation-on-graphs","title":"Attention-Based Recommendation On Graphs","date":"2022-01-04","arxiv_id":"2201.05499","n_code_links":0,"syntology":null},{"paper":"/paper/node-aligned-graph-convolutional-network-for","slug":"node-aligned-graph-convolutional-network-for","title":"Node-Aligned Graph Convolutional Network for Whole-Slide Image Representation and Classification","date":"2022-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/are-we-really-making-much-progress-revisiting","slug":"are-we-really-making-much-progress-revisiting","title":"Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks","date":"2021-12-30","arxiv_id":"2112.14936","n_code_links":2,"syntology":null},{"paper":"/paper/deep-graph-clustering-via-dual-correlation","slug":"deep-graph-clustering-via-dual-correlation","title":"Deep Graph Clustering via Dual Correlation Reduction","date":"2021-12-29","arxiv_id":"2112.14772","n_code_links":2,"syntology":null},{"paper":null,"slug":"powerful-graph-convolutioal-networks-with","title":"Powerful Graph Convolutioal Networks with Adaptive Propagation Mechanism for Homophily and Heterophily","date":"2021-12-27","arxiv_id":"2112.13562","n_code_links":0,"syntology":null},{"paper":"/paper/gcod-graph-convolutional-network-acceleration","slug":"gcod-graph-convolutional-network-acceleration","title":"GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design","date":"2021-12-22","arxiv_id":"2112.11594","n_code_links":1,"syntology":{"ran":3,"of":8,"n_ran_checked":2,"n_instrument":1,"unverified":5,"pointer_only":0,"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) · 5 unverified","official":{"repos":["rice-eic/gcod"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":"/paper/skipnode-on-alleviating-over-smoothing-for","slug":"skipnode-on-alleviating-over-smoothing-for","title":"SkipNode: On Alleviating Performance Degradation for Deep Graph Convolutional Networks","date":"2021-12-22","arxiv_id":"2112.11628","n_code_links":1,"syntology":{"ran":7,"of":7,"n_ran_checked":6,"n_instrument":1,"unverified":0,"pointer_only":1,"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) · 0 unverified","official":{"repos":["weiganglu/skipnode"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"fedni-federated-graph-learning-with-network","title":"FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction","date":"2021-12-19","arxiv_id":"2112.10166","n_code_links":0,"syntology":null},{"paper":null,"slug":"gcn-geo-a-graph-convolution-network-based","title":"GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Framework","date":"2021-12-18","arxiv_id":"2112.10767","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-attention-based-anchor-proposal-for","title":"Self-attention based anchor proposal for skeleton-based action recognition","date":"2021-12-17","arxiv_id":"2112.09413","n_code_links":0,"syntology":null},{"paper":"/paper/gcndepth-self-supervised-monocular-depth","slug":"gcndepth-self-supervised-monocular-depth","title":"GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network","date":"2021-12-13","arxiv_id":"2112.06782","n_code_links":1,"syntology":null},{"paper":"/paper/attacking-point-cloud-segmentation-with-color","slug":"attacking-point-cloud-segmentation-with-color","title":"On Adversarial Robustness of Point Cloud Semantic Segmentation","date":"2021-12-11","arxiv_id":"2112.05871","n_code_links":1,"syntology":null},{"paper":"/paper/ccasgnn-collaborative-cascade-prediction","slug":"ccasgnn-collaborative-cascade-prediction","title":"CCasGNN: Collaborative Cascade Prediction Based on Graph Neural Networks","date":"2021-12-07","arxiv_id":"2112.03644","n_code_links":1,"syntology":null},{"paper":"/paper/graph-neural-controlled-differential","slug":"graph-neural-controlled-differential","title":"Graph Neural Controlled Differential Equations for Traffic Forecasting","date":"2021-12-07","arxiv_id":"2112.03558","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-effective-gcn-based-hierarchical-multi-1","title":"An Effective GCN-based Hierarchical Multi-label classification for Protein Function Prediction","date":"2021-12-06","arxiv_id":"2112.02810","n_code_links":0,"syntology":null},{"paper":null,"slug":"cdgnet-a-cross-time-dynamic-graph-based-deep","title":"CDGNet: A Cross-Time Dynamic Graph-based Deep Learning Model for Traffic Forecasting","date":"2021-12-06","arxiv_id":"2112.02736","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-scale-graph-convolutional-networks-with","title":"Multi-scale Graph Convolutional Networks with Self-Attention","date":"2021-12-04","arxiv_id":"2112.03262","n_code_links":0,"syntology":null},{"paper":null,"slug":"extraction-of-diverse-gene-groups-with","title":"Extraction of diverse gene groups with individual relationship from gene co-expression networks","date":"2021-12-02","arxiv_id":"2112.01180","n_code_links":0,"syntology":null},{"paper":"/paper/universal-graph-convolutional-networks","slug":"universal-graph-convolutional-networks","title":"Universal Graph Convolutional Networks","date":"2021-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/climart-a-benchmark-dataset-for-emulating","slug":"climart-a-benchmark-dataset-for-emulating","title":"ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models","date":"2021-11-29","arxiv_id":"2111.14671","n_code_links":1,"syntology":null},{"paper":"/paper/diffconv-analyzing-irregular-point-clouds","slug":"diffconv-analyzing-irregular-point-clouds","title":"diffConv: Analyzing Irregular Point Clouds with an Irregular View","date":"2021-11-29","arxiv_id":"2111.14658","n_code_links":1,"syntology":null},{"paper":null,"slug":"quaternion-based-graph-convolution-network","title":"Quaternion-Based Graph Convolution Network for Recommendation","date":"2021-11-20","arxiv_id":"2111.10536","n_code_links":0,"syntology":null},{"paper":"/paper/coarse-to-fine-animal-pose-and-shape","slug":"coarse-to-fine-animal-pose-and-shape","title":"Coarse-to-fine Animal Pose and Shape Estimation","date":"2021-11-16","arxiv_id":"2111.08176","n_code_links":1,"syntology":{"ran":13,"of":14,"n_ran_checked":13,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["chaneyddtt/coarse-to-fine-3d-animal"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"gause-gaussian-enhanced-self-attention-for","title":"GauSE: Gaussian Enhanced Self-Attention for Event Extraction","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/keypoint-message-passing-for-video-based","slug":"keypoint-message-passing-for-video-based","title":"Keypoint Message Passing for Video-based Person Re-Identification","date":"2021-11-16","arxiv_id":"2111.08279","n_code_links":1,"syntology":null},{"paper":null,"slug":"sstagcn-simplified-stacking-based-graph","title":"SStaGCN: Simplified stacking based graph convolutional networks","date":"2021-11-16","arxiv_id":"2111.08228","n_code_links":0,"syntology":null},{"paper":null,"slug":"translating-embeddings-in-document-for","title":"Translating Embeddings in Document for Modeling Multi-relational Graphs","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"short-term-power-prediction-for-renewable","title":"Short-Term Power Prediction for Renewable Energy Using Hybrid Graph Convolutional Network and Long Short-Term Memory Approach","date":"2021-11-15","arxiv_id":"2111.07958","n_code_links":0,"syntology":null},{"paper":"/paper/spectral-transform-forms-scalable-transformer","slug":"spectral-transform-forms-scalable-transformer","title":"Spectral Transform Forms Scalable Transformer","date":"2021-11-15","arxiv_id":"2111.07602","n_code_links":1,"syntology":null},{"paper":"/paper/linear-or-non-linear-that-is-the-question","slug":"linear-or-non-linear-that-is-the-question","title":"Linear, or Non-Linear, That is the Question!","date":"2021-11-14","arxiv_id":"2111.07265","n_code_links":2,"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: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["jeongwhanchoi/HMLET"],"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/delay-oriented-distributed-scheduling-using","slug":"delay-oriented-distributed-scheduling-using","title":"Delay-Oriented Distributed Scheduling Using Graph Neural Networks","date":"2021-11-13","arxiv_id":"2111.07017","n_code_links":1,"syntology":null},{"paper":null,"slug":"anchorgae-general-data-clustering-via-o-n","title":"AnchorGAE: General Data Clustering via $O(n)$ Bipartite Graph Convolution","date":"2021-11-12","arxiv_id":"2111.06586","n_code_links":0,"syntology":null},{"paper":null,"slug":"monocular-human-shape-and-pose-with-dense","title":"Monocular Human Shape and Pose with Dense Mesh-borne Local Image Features","date":"2021-11-09","arxiv_id":"2111.05319","n_code_links":0,"syntology":null},{"paper":null,"slug":"lw-gcn-a-lightweight-fpga-based-graph","title":"LW-GCN: A Lightweight FPGA-based Graph Convolutional Network Accelerator","date":"2021-11-04","arxiv_id":"2111.03184","n_code_links":0,"syntology":null},{"paper":null,"slug":"fedgraph-federated-graph-learning-with","title":"FedGraph: Federated Graph Learning with Intelligent Sampling","date":"2021-11-02","arxiv_id":"2111.01370","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptive-multi-receptive-field-spatial","title":"Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Network for Traffic Forecasting","date":"2021-11-01","arxiv_id":"2111.00724","n_code_links":0,"syntology":null},{"paper":null,"slug":"gcnear-a-hybrid-architecture-for-efficient","title":"GNNear: Accelerating Full-Batch Training of Graph Neural Networks with Near-Memory Processing","date":"2021-11-01","arxiv_id":"2111.00680","n_code_links":0,"syntology":null},{"paper":null,"slug":"graph-neural-network-based-scheduling","title":"Graph Neural Network based scheduling : Improved throughput under a generalized interference model","date":"2021-10-31","arxiv_id":"2111.00459","n_code_links":0,"syntology":null},{"paper":"/paper/rim-reliable-influence-based-active-learning","slug":"rim-reliable-influence-based-active-learning","title":"RIM: Reliable Influence-based Active Learning on Graphs","date":"2021-10-28","arxiv_id":"2110.14854","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: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["zwt233/rim"],"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/ultragcn-ultra-simplification-of-graph","slug":"ultragcn-ultra-simplification-of-graph","title":"UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation","date":"2021-10-28","arxiv_id":"2110.15114","n_code_links":2,"syntology":null},{"paper":null,"slug":"gacan-graph-attention-convolution-attention","title":"GACAN: Graph Attention-Convolution-Attention Networks for Traffic Forecasting Based on Multi-granularity Time Series","date":"2021-10-27","arxiv_id":"2110.14331","n_code_links":0,"syntology":null},{"paper":"/paper/node-dependent-local-smoothing-for-scalable","slug":"node-dependent-local-smoothing-for-scalable","title":"Node Dependent Local Smoothing for Scalable Graph Learning","date":"2021-10-27","arxiv_id":"2110.14377","n_code_links":1,"syntology":null},{"paper":null,"slug":"creating-and-reenacting-controllable-3d","title":"Creating and Reenacting Controllable 3D Humans with Differentiable Rendering","date":"2021-10-22","arxiv_id":"2110.11746","n_code_links":0,"syntology":null},{"paper":null,"slug":"gcnscheduler-scheduling-distributed-computing","title":"GCNScheduler: Scheduling Distributed Computing Applications using Graph Convolutional Networks","date":"2021-10-22","arxiv_id":"2110.11552","n_code_links":0,"syntology":null}],"record_sha256":"28bbb02fb66136f3a6eea433a37dcfd5199413757eaa6ddea7c3c43c9c2b2c55","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}