{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/graph-representation-learning/papers/10","list_of":"/task/graph-representation-learning","task":"Graph Representation Learning","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":10,"pages_in_order":10,"rows_per_page":100,"rows":[901,982],"of":982,"counts":{"archive_papers_tagged":982,"with_a_code_link":479,"where_syntology_ran_a_sample":129,"not_listed_spam_title":0,"listed":982,"listed_where_code_ran":129,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":115,"every_run_a_failure_of_syntologys_instrument":14,"listed_with_a_run_with_no_instrument_failure":115,"listed_every_run_a_failure_of_syntologys_instrument":14,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/graph-representation-learning","prev":"/task/graph-representation-learning/papers/9","next":null,"papers":[{"url":null,"slug":"deep-multi-attribute-graph-representation","title":"Deep Multi-attribute Graph Representation Learning on Protein Structures","date":"2020-12-22","arxiv_id":"2012.11762","repositories_listed":0,"syntology":null},{"url":null,"slug":"hop-hop-relation-aware-graph-neural-networks","title":"Hop-Hop Relation-aware Graph Neural Networks","date":"2020-12-21","arxiv_id":"2012.11147","repositories_listed":0,"syntology":null},{"url":null,"slug":"biomedical-knowledge-graph-refinement-and","title":"Biomedical Knowledge Graph Refinement and Completion using Graph Representation Learning and Top-K Similarity Measure","date":"2020-12-18","arxiv_id":"2012.10540","repositories_listed":0,"syntology":null},{"url":null,"slug":"pair-view-unsupervised-graph-representation","title":"Pair-view Unsupervised Graph Representation Learning","date":"2020-12-11","arxiv_id":"2012.06113","repositories_listed":0,"syntology":null},{"url":null,"slug":"commpool-an-interpretable-graph-pooling","title":"CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning","date":"2020-12-10","arxiv_id":"2012.05980","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-adversarially-robust","title":"Unsupervised Adversarially-Robust Representation Learning on Graphs","date":"2020-12-04","arxiv_id":"2012.02486","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-heterogeneous-graph-embedding","title":"A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources","date":"2020-11-30","arxiv_id":"2011.14867","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-data-driven-study-of-commonsense-knowledge","title":"A Data-Driven Study of Commonsense Knowledge using the ConceptNet Knowledge Base","date":"2020-11-28","arxiv_id":"2011.14084","repositories_listed":0,"syntology":null},{"url":null,"slug":"relation-weighted-link-prediction-for-disease","title":"Relation-weighted Link Prediction for Disease Gene Identification","date":"2020-11-10","arxiv_id":"2011.05138","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-graph-representation-learning-1","title":"Self-supervised Graph Representation Learning via Bootstrapping","date":"2020-11-10","arxiv_id":"2011.05126","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-active-graph-representation-learning","title":"When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision","date":"2020-10-30","arxiv_id":"2010.16091","repositories_listed":0,"syntology":null},{"url":null,"slug":"persgnn-applying-topological-data-analysis","title":"PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction","date":"2020-10-30","arxiv_id":"2010.16027","repositories_listed":0,"syntology":null},{"url":null,"slug":"xlvin-executed-latent-value-iteration-nets-1","title":"XLVIN: eXecuted Latent Value Iteration Nets","date":"2020-10-25","arxiv_id":"2010.13146","repositories_listed":0,"syntology":null},{"url":null,"slug":"decentralized-knowledge-graph-representation-1","title":"Distributed Representations of Entities in Open-World Knowledge Graphs","date":"2020-10-16","arxiv_id":"2010.08114","repositories_listed":0,"syntology":null},{"url":null,"slug":"multivariate-time-series-classification-with-1","title":"Multivariate Time Series Classification with Hierarchical Variational Graph Pooling","date":"2020-10-12","arxiv_id":"2010.05649","repositories_listed":0,"syntology":null},{"url":null,"slug":"rm-n-small-ode-s-small-ig-random-walk","title":"NodeSig: Binary Node Embeddings via Random Walk Diffusion","date":"2020-10-01","arxiv_id":"2010.00261","repositories_listed":0,"syntology":null},{"url":null,"slug":"div2vec-diversity-emphasized-node-embedding","title":"div2vec: Diversity-Emphasized Node Embedding","date":"2020-09-21","arxiv_id":"2009.09588","repositories_listed":0,"syntology":null},{"url":null,"slug":"polyp-artifact-relationship-analysis-using","title":"Polyp-artifact relationship analysis using graph inductive learned representations","date":"2020-09-15","arxiv_id":"2009.07109","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-disease-self-diagnosis-with-inductive","title":"Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks","date":"2020-09-06","arxiv_id":"2009.02625","repositories_listed":0,"syntology":null},{"url":null,"slug":"cagnn-cluster-aware-graph-neural-networks-for","title":"CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning","date":"2020-09-03","arxiv_id":"2009.01674","repositories_listed":0,"syntology":null},{"url":null,"slug":"offer-a-motif-dimensional-framework-for","title":"OFFER: A Motif Dimensional Framework for Network Representation Learning","date":"2020-08-27","arxiv_id":"2008.12010","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-embedding-for-combinatorial","title":"Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art","date":"2020-08-26","arxiv_id":"2008.12646","repositories_listed":0,"syntology":null},{"url":null,"slug":"tree-structure-aware-graph-representation","title":"Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning","date":"2020-08-23","arxiv_id":"2008.10003","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-knowledge-graph-validation-via","title":"Efficient Knowledge Graph Validation via Cross-Graph Representation Learning","date":"2020-08-16","arxiv_id":"2008.06995","repositories_listed":0,"syntology":null},{"url":null,"slug":"concentration-bounds-for-co-occurrence","title":"A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices","date":"2020-08-06","arxiv_id":"2008.02464","repositories_listed":0,"syntology":null},{"url":null,"slug":"node2coords-graph-representation-learning","title":"node2coords: Graph Representation Learning with Wasserstein Barycenters","date":"2020-07-31","arxiv_id":"2007.16056","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-representation-learning-for-multimodal","title":"Deep Representation Learning For Multimodal Brain Networks","date":"2020-07-19","arxiv_id":"2007.09777","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-hyperbolic-representations-in-graphs","title":"Are Hyperbolic Representations in Graphs Created Equal?","date":"2020-07-15","arxiv_id":"2007.07698","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatio-temporal-scene-graphs-for-video-dialog","title":"Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers","date":"2020-07-08","arxiv_id":"2007.03848","repositories_listed":0,"syntology":null},{"url":null,"slug":"navigating-the-dynamics-of-financial","title":"Navigating the Dynamics of Financial Embeddings over Time","date":"2020-07-01","arxiv_id":"2007.00591","repositories_listed":0,"syntology":null},{"url":null,"slug":"structural-landmarking-and-interaction","title":"Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification","date":"2020-06-29","arxiv_id":"2006.15763","repositories_listed":0,"syntology":null},{"url":null,"slug":"hop-sampling-a-simple-regularized-graph","title":"Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments","date":"2020-06-26","arxiv_id":"2006.14897","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-challenges-in-the-application-of","title":"Quantifying Challenges in the Application of Graph Representation Learning","date":"2020-06-18","arxiv_id":"2006.10252","repositories_listed":0,"syntology":null},{"url":null,"slug":"g5-a-universal-graph-bert-for-graph-to-graph","title":"G5: A Universal GRAPH-BERT for Graph-to-Graph Transfer and Apocalypse Learning","date":"2020-06-11","arxiv_id":"2006.06183","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-attack-on-hierarchical-graph","title":"Adversarial Attack on Hierarchical Graph Pooling Neural Networks","date":"2020-05-23","arxiv_id":"2005.11560","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-gene-expression-from-network","title":"A Graph Feature Auto-Encoder for the Prediction of Unobserved Node Features on Biological Networks","date":"2020-05-08","arxiv_id":"2005.03961","repositories_listed":0,"syntology":null},{"url":null,"slug":"mxpool-multiplex-pooling-for-hierarchical-1","title":"MxPool: Multiplex Pooling for Hierarchical Graph Representation Learning","date":"2020-04-15","arxiv_id":"2004.06846","repositories_listed":0,"syntology":null},{"url":null,"slug":"sac-accelerating-and-structuring-self","title":"SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection","date":"2020-03-22","arxiv_id":"2003.09833","repositories_listed":0,"syntology":null},{"url":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","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-hash-with-graph-neural-networks","title":"Learning to Hash with Graph Neural Networks for Recommender Systems","date":"2020-03-04","arxiv_id":"2003.01917","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-graph-representation-learning","title":"Self-Supervised Graph Representation Learning via Global Context Prediction","date":"2020-03-03","arxiv_id":"2003.01604","repositories_listed":0,"syntology":null},{"url":null,"slug":"semiparametric-nonlinear-bipartite-graph","title":"Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees","date":"2020-03-02","arxiv_id":"2003.01013","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-representation-learning-for-merchant","title":"Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing","date":"2020-02-27","arxiv_id":"2003.01515","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-graph-representation-learning","title":"Dual Graph Representation Learning","date":"2020-02-25","arxiv_id":"2002.11501","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-power-of-graph-convolutional-networks-to-1","title":"The Power of Graph Convolutional Networks to Distinguish Random Graph Models: Short Version","date":"2020-02-13","arxiv_id":"2002.05678","repositories_listed":0,"syntology":null},{"url":null,"slug":"hgat-hierarchical-graph-attention-network-for","title":"Fake News Detection on News-Oriented Heterogeneous Information Networks through Hierarchical Graph Attention","date":"2020-02-05","arxiv_id":"2002.04397","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-graph-representation-learning-in","title":"Towards Graph Representation Learning in Emergent Communication","date":"2020-01-24","arxiv_id":"2001.09063","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-ordering-towards-the-optimal-by","title":"Graph Ordering: Towards the Optimal by Learning","date":"2020-01-18","arxiv_id":"2001.06631","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-channel-graph-convolutional-networks","title":"Multi-Channel Graph Convolutional Networks","date":"2019-12-17","arxiv_id":"1912.08306","repositories_listed":0,"syntology":null},{"url":null,"slug":"graphposegan-3d-hand-pose-estimation-from-a","title":"3D Hand Pose Estimation via Regularized Graph Representation Learning","date":"2019-12-04","arxiv_id":"1912.01875","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-node-features-for-graph-neural-networks","title":"On Node Features for Graph Neural Networks","date":"2019-11-20","arxiv_id":"1911.08795","repositories_listed":0,"syntology":null},{"url":null,"slug":"gralsp-graph-neural-networks-with-local","title":"GraLSP: Graph Neural Networks with Local Structural Patterns","date":"2019-11-18","arxiv_id":"1911.07675","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-representation-learning-via-multi-task","title":"Graph Representation Learning via Multi-task Knowledge Distillation","date":"2019-11-11","arxiv_id":"1911.05700","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-power-of-graph-convolutional-networks-to","title":"Fundamental Limits of Deep Graph Convolutional Networks","date":"2019-10-28","arxiv_id":"1910.12954","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-representation-learning-for-audio-music","title":"Graph Representation learning for Audio & Music genre Classification","date":"2019-10-23","arxiv_id":"1910.11117","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoupling-feature-propagation-from-the","title":"Decoupling feature propagation from the design of graph auto-encoders","date":"2019-10-18","arxiv_id":"1910.08589","repositories_listed":0,"syntology":null},{"url":null,"slug":"relational-graph-representation-learning-for","title":"Relational Graph Representation Learning for Open-Domain Question Answering","date":"2019-10-18","arxiv_id":"1910.08249","repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangling-interpretable-generative","title":"Disentangling Interpretable Generative Parameters of Random and Real-World Graphs","date":"2019-10-12","arxiv_id":"1910.05639","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-interpretability-and-evaluation-of","title":"On the Interpretability and Evaluation of Graph Representation Learning","date":"2019-10-07","arxiv_id":"1910.03081","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-robust-representations-with-graph","title":"Learning Robust Representations with Graph Denoising Policy Network","date":"2019-10-04","arxiv_id":"1910.01784","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bi-diffusion-based-layer-wise-sampling","title":"A bi-diffusion based layer-wise sampling method for deep learning in large graphs","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"empowering-graph-representation-learning-with","title":"Empowering Graph Representation Learning with Paired Training and Graph Co-Attention","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-interpretable-molecular-graph","title":"Towards Interpretable Molecular Graph Representation Learning","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-hierarchical-graph","title":"Unsupervised Hierarchical Graph Representation Learning with Variational Bayes","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"chainnet-learning-on-blockchain-graphs-with","title":"ChainNet: Learning on Blockchain Graphs with Topological Features","date":"2019-08-18","arxiv_id":"1908.06971","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-event-propagation-via-graph-biased","title":"Modeling Event Propagation via Graph Biased Temporal Point Process","date":"2019-08-05","arxiv_id":"1908.01623","repositories_listed":0,"syntology":null},{"url":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","repositories_listed":0,"syntology":null},{"url":null,"slug":"deeptrax-embedding-graphs-of-financial","title":"DeepTrax: Embedding Graphs of Financial Transactions","date":"2019-07-16","arxiv_id":"1907.07225","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-illicit-accounts-in-large-scale-e","title":"Identifying Illicit Accounts in Large Scale E-payment Networks -- A Graph Representation Learning Approach","date":"2019-06-13","arxiv_id":"1906.05546","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-interpretable-sparse-graph","title":"Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling","date":"2019-05-28","arxiv_id":"1905.11577","repositories_listed":0,"syntology":null},{"url":null,"slug":"inductive-graph-representation-learning-with","title":"Residual or Gate? Towards Deeper Graph Neural Networks for Inductive Graph Representation Learning","date":"2019-04-17","arxiv_id":"1904.08035","repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-learning-for-spatial-graphs","title":"Representation Learning for Spatial Graphs","date":"2018-12-17","arxiv_id":"1812.06668","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-classifier-for-imbalanced","title":"Adversarial Classifier for Imbalanced Problems","date":"2018-11-21","arxiv_id":"1811.08812","repositories_listed":0,"syntology":null},{"url":"/paper/discriminative-graph-autoencoder","slug":"discriminative-graph-autoencoder","title":"Discriminative Graph Autoencoder","date":"2018-11-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sgr-self-supervised-spectral-graph","title":"SGR: Self-Supervised Spectral Graph Representation Learning","date":"2018-11-15","arxiv_id":"1811.06237","repositories_listed":0,"syntology":null},{"url":null,"slug":"linknbed-multi-graph-representation-learning","title":"LinkNBed: Multi-Graph Representation Learning with Entity Linkage","date":"2018-07-23","arxiv_id":"1807.08447","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multimodal-translation-based-approach-for","title":"A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"accurate-text-enhanced-knowledge-graph","title":"Accurate Text-Enhanced Knowledge Graph Representation Learning","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"gesf-a-universal-discriminative-mapping","title":"GESF: A Universal Discriminative Mapping Mechanism for Graph Representation Learning","date":"2018-05-28","arxiv_id":"1805.11182","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-propagation-on-graph-a-new","title":"Feature Propagation on Graph: A New Perspective to Graph Representation Learning","date":"2018-04-17","arxiv_id":"1804.06111","repositories_listed":0,"syntology":null},{"url":null,"slug":"marginalized-graph-autoencoder-for-graph","title":"Marginalized graph autoencoder for graph clustering","date":"2017-11-06","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-feature-learning-for-graphs","title":"Deep Feature Learning for Graphs","date":"2017-04-28","arxiv_id":"1704.08829","repositories_listed":0,"syntology":null}],"record_sha256":"258f6d5ed41b58aa16ea3c6255c5d6f43d971d109995e40b7b70ca9b4dd599d7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}