{"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/8","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":8,"pages_in_order":10,"rows_per_page":100,"rows":[701,800],"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/7","next":"/task/graph-representation-learning/papers/9","papers":[{"url":null,"slug":"prior-information-based-decomposition-and","title":"Prior Information based Decomposition and Reconstruction Learning for Micro-Expression Recognition","date":"2023-03-03","arxiv_id":"2303.01776","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-dataset-for-learning-graph-representations","title":"A Dataset for Learning Graph Representations to Predict Customer Returns in Fashion Retail","date":"2023-02-27","arxiv_id":"2302.14096","repositories_listed":0,"syntology":null},{"url":null,"slug":"drop-edges-and-adapt-a-fairness-enforcing","title":"Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks","date":"2023-02-22","arxiv_id":"2302.11479","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-dynamic-graph-embeddings-with-neural","title":"Learning Dynamic Graph Embeddings with Neural Controlled Differential Equations","date":"2023-02-22","arxiv_id":"2302.11354","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-general-purpose-transferable-predictor-for","title":"A General-Purpose Transferable Predictor for Neural Architecture Search","date":"2023-02-21","arxiv_id":"2302.10835","repositories_listed":0,"syntology":null},{"url":null,"slug":"creating-generalizable-downstream-graph","title":"Creating generalizable downstream graph models with random projections","date":"2023-02-17","arxiv_id":"2302.08895","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficiently-forgetting-what-you-have-learned","title":"Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection","date":"2023-02-17","arxiv_id":"2302.08990","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-spectral-graph-neural-networks","title":"A Survey on Spectral Graph Neural Networks","date":"2023-02-11","arxiv_id":"2302.05631","repositories_listed":0,"syntology":null},{"url":null,"slug":"heterophily-aware-graph-attention-network","title":"Heterophily-Aware Graph Attention Network","date":"2023-02-07","arxiv_id":"2302.03228","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-augmentations-for-graph-contrastive","title":"Spectral Augmentations for Graph Contrastive Learning","date":"2023-02-06","arxiv_id":"2302.02909","repositories_listed":0,"syntology":null},{"url":null,"slug":"grande-a-neural-model-over-directed","title":"GRANDE: a neural model over directed multigraphs with application to anti-money laundering","date":"2023-02-04","arxiv_id":"2302.02101","repositories_listed":0,"syntology":null},{"url":null,"slug":"simple-yet-effective-gradient-free-graph","title":"Simple yet Effective Gradient-Free Graph Convolutional Networks","date":"2023-02-01","arxiv_id":"2302.00371","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-based-time-series-anomaly-detection-a","title":"Graph Anomaly Detection in Time Series: A Survey","date":"2023-01-31","arxiv_id":"2302.00058","repositories_listed":0,"syntology":null},{"url":null,"slug":"hat-gae-self-supervised-graph-auto-encoders","title":"HAT-GAE: Self-Supervised Graph Auto-encoders with Hierarchical Adaptive Masking and Trainable Corruption","date":"2023-01-28","arxiv_id":"2301.12063","repositories_listed":0,"syntology":null},{"url":null,"slug":"uplink-scheduling-in-federated-learning-an","title":"Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning","date":"2023-01-27","arxiv_id":"2301.11903","repositories_listed":0,"syntology":null},{"url":null,"slug":"sterling-synergistic-representation-learning","title":"STERLING: Synergistic Representation Learning on Bipartite Graphs","date":"2023-01-25","arxiv_id":"2302.05428","repositories_listed":0,"syntology":null},{"url":null,"slug":"introducing-expertise-logic-into-graph","title":"Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective","date":"2023-01-20","arxiv_id":"2301.08496","repositories_listed":0,"syntology":null},{"url":null,"slug":"everything-is-connected-graph-neural-networks","title":"Everything is Connected: Graph Neural Networks","date":"2023-01-19","arxiv_id":"2301.08210","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-few-shot-knowledge-graph","title":"A Survey On Few-shot Knowledge Graph Completion with Structural and Commonsense Knowledge","date":"2023-01-03","arxiv_id":"2301.01172","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-mooc-quality-evaluation-via","title":"Multi-View MOOC Quality Evaluation via Information-Aware Graph Representation Learning","date":"2023-01-01","arxiv_id":"2301.01593","repositories_listed":0,"syntology":null},{"url":null,"slug":"piecewise-velocity-model-for-learning","title":"Piecewise-Velocity Model for Learning Continuous-time Dynamic Node Representations","date":"2022-12-23","arxiv_id":"2212.12345","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-learning-with-localized-neighborhood","title":"Graph Learning with Localized Neighborhood Fairness","date":"2022-12-22","arxiv_id":"2212.12040","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-graph-representation-learning-via-1","title":"Robust Graph Representation Learning via Predictive Coding","date":"2022-12-09","arxiv_id":"2212.04656","repositories_listed":0,"syntology":null},{"url":null,"slug":"alleviating-neighbor-bias-augmenting-graph","title":"Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples","date":"2022-12-08","arxiv_id":"2212.04365","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-graph-search-heuristics","title":"Learning Graph Search Heuristics","date":"2022-12-07","arxiv_id":"2212.03978","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-graph-representation-learning-3","title":"Self-supervised Graph Representation Learning for Black Market Account Detection","date":"2022-12-06","arxiv_id":"2212.02679","repositories_listed":0,"syntology":null},{"url":null,"slug":"coordinating-cross-modal-distillation-for","title":"Coordinating Cross-modal Distillation for Molecular Property Prediction","date":"2022-11-30","arxiv_id":"2211.16712","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-relational-bias-on-knowledge","title":"Mitigating Relational Bias on Knowledge Graphs","date":"2022-11-26","arxiv_id":"2211.14489","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-wind-turbine-wake-modelling-with","title":"End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learning","date":"2022-11-24","arxiv_id":"2211.13649","repositories_listed":0,"syntology":null},{"url":"/paper/enhancing-intra-class-information-extraction","slug":"enhancing-intra-class-information-extraction","title":"Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach","date":"2022-11-20","arxiv_id":"2211.10990","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-generalizable-graph-contrastive","title":"Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective","date":"2022-11-20","arxiv_id":"2211.10929","repositories_listed":0,"syntology":null},{"url":null,"slug":"eden-a-plug-in-equivariant-distance-encoding","title":"EDEN: A Plug-in Equivariant Distance Encoding to Beyond the 1-WL Test","date":"2022-11-19","arxiv_id":"2211.10739","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-multi-neighborhood-attention-based","title":"Adaptive Multi-Neighborhood Attention based Transformer for Graph Representation Learning","date":"2022-11-15","arxiv_id":"2211.07970","repositories_listed":0,"syntology":null},{"url":null,"slug":"neighborhood-convolutional-network-a-new","title":"Neighborhood Convolutional Network: A New Paradigm of Graph Neural Networks for Node Classification","date":"2022-11-15","arxiv_id":"2211.07845","repositories_listed":0,"syntology":null},{"url":null,"slug":"holder-recommendations-using-graph","title":"Holder Recommendations using Graph Representation Learning & Link Prediction","date":"2022-11-10","arxiv_id":"2212.09624","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-representation-learning-for-street","title":"Graph representation learning for street networks","date":"2022-11-09","arxiv_id":"2211.04984","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperbolic-graph-representation-learning-a","title":"Hyperbolic Graph Representation Learning: A Tutorial","date":"2022-11-08","arxiv_id":"2211.04050","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-graph-neural-networks-and","title":"Application of Graph Neural Networks and graph descriptors for graph classification","date":"2022-11-07","arxiv_id":"2211.03666","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-orbital-information-and-atomic","title":"Leveraging Orbital Information and Atomic Feature in Deep Learning Model","date":"2022-10-29","arxiv_id":"2211.11543","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-laplacian-positional-encoding-for","title":"Generalized Laplacian Positional Encoding for Graph Representation Learning","date":"2022-10-28","arxiv_id":"2210.15956","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-graph-representation-learning-using","title":"Federated Graph Representation Learning using Self-Supervision","date":"2022-10-27","arxiv_id":"2210.15120","repositories_listed":0,"syntology":null},{"url":null,"slug":"implications-of-sparsity-and-high-triangle","title":"Implications of sparsity and high triangle density for graph representation learning","date":"2022-10-27","arxiv_id":"2210.15277","repositories_listed":0,"syntology":null},{"url":null,"slug":"laundrograph-self-supervised-graph","title":"LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering","date":"2022-10-25","arxiv_id":"2210.14360","repositories_listed":0,"syntology":null},{"url":null,"slug":"spiking-variational-graph-auto-encoders-for","title":"Spiking Variational Graph Auto-Encoders for Efficient Graph Representation Learning","date":"2022-10-24","arxiv_id":"2211.01952","repositories_listed":0,"syntology":null},{"url":null,"slug":"hcl-improving-graph-representation-with","title":"HCL: Improving Graph Representation with Hierarchical Contrastive Learning","date":"2022-10-21","arxiv_id":"2210.12020","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-sampling-for-node-embedding","title":"Graph sampling for node embedding","date":"2022-10-19","arxiv_id":"2210.10520","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-brief-survey-on-representation-learning","title":"A Brief Survey on Representation Learning based Graph Dimensionality Reduction Techniques","date":"2022-10-13","arxiv_id":"2211.05594","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-graph-based-text-representations","title":"Improving Graph-Based Text Representations with Character and Word Level N-grams","date":"2022-10-12","arxiv_id":"2210.05999","repositories_listed":0,"syntology":null},{"url":"/paper/break-the-wall-between-homophily-and","slug":"break-the-wall-between-homophily-and","title":"Uplifting Message Passing Neural Network with Graph Original Information","date":"2022-10-08","arxiv_id":"2210.05382","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-graph-self-supervised-learning-via","title":"Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation","date":"2022-10-05","arxiv_id":"2210.02099","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-substructures-in-commonsense","title":"Understanding Substructures in Commonsense Relations in ConceptNet","date":"2022-10-03","arxiv_id":"2210.01263","repositories_listed":0,"syntology":null},{"url":null,"slug":"diving-into-unified-data-model-sparsity-for","title":"Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning","date":"2022-10-01","arxiv_id":"2210.00162","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-graph-neural-networks-and-graph","title":"A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective","date":"2022-09-27","arxiv_id":"2209.13232","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-representation-learning-for-energy","title":"Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints","date":"2022-09-24","arxiv_id":"2209.12035","repositories_listed":0,"syntology":null},{"url":null,"slug":"scgg-a-deep-structure-conditioned-graph","title":"SCGG: A Deep Structure-Conditioned Graph Generative Model","date":"2022-09-20","arxiv_id":"2209.09681","repositories_listed":0,"syntology":null},{"url":null,"slug":"reviewing-embeddings-for-graph-neural","title":"Revisiting Embeddings for Graph Neural Networks","date":"2022-09-19","arxiv_id":"2209.09338","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-partners-in-criminal","title":"Machine Learning Partners in Criminal Networks","date":"2022-09-07","arxiv_id":"2209.03171","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-class-aware-representation-refinement","title":"A Class-Aware Representation Refinement Framework for Graph Classification","date":"2022-09-02","arxiv_id":"2209.00936","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-self-supervised-riemannian-gnn-with-time","title":"A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning","date":"2022-08-30","arxiv_id":"2208.14073","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-temporal-graph-representation","title":"A Survey on Temporal Graph Representation Learning and Generative Modeling","date":"2022-08-25","arxiv_id":"2208.12126","repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-learning-on-graphs-to","title":"Representation Learning on Graphs to Identifying Circular Trading in Goods and Services Tax","date":"2022-08-16","arxiv_id":"2208.07660","repositories_listed":0,"syntology":null},{"url":null,"slug":"aminergnn-heterogeneous-graph-neural-network","title":"AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query","date":"2022-08-15","arxiv_id":"2208.07201","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-learning-on-small-data","title":"A Survey of Learning on Small Data: Generalization, Optimization, and Challenge","date":"2022-07-29","arxiv_id":"2207.14443","repositories_listed":0,"syntology":null},{"url":null,"slug":"octal-graph-representation-learning-for-ltl","title":"OCTAL: Graph Representation Learning for LTL Model Checking","date":"2022-07-24","arxiv_id":"2207.11649","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-agnostic-and-diverse-explanations-for","title":"Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs","date":"2022-07-17","arxiv_id":"2207.08098","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-brain-network-learning-via","title":"Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model","date":"2022-07-14","arxiv_id":"2207.07650","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-node-embeddings-via-summary-graphs-a","title":"Learning node embeddings via summary graphs: a brief theoretical analysis","date":"2022-07-04","arxiv_id":"2207.01189","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-counterfactual-hard-negative","title":"Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning","date":"2022-07-01","arxiv_id":"2207.00148","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-machine-learning-a-survey-and-open","title":"Causal Machine Learning: A Survey and Open Problems","date":"2022-06-30","arxiv_id":"2206.15475","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-community-detection-via-adversarial","title":"Dynamic Community Detection via Adversarial Temporal Graph Representation Learning","date":"2022-06-29","arxiv_id":"2207.03580","repositories_listed":0,"syntology":null},{"url":null,"slug":"multisage-a-multiplex-embedding-algorithm-for","title":"MultiSAGE: a multiplex embedding algorithm for inter-layer link prediction","date":"2022-06-24","arxiv_id":"2206.13223","repositories_listed":0,"syntology":null},{"url":null,"slug":"transferable-graph-backdoor-attack","title":"Transferable Graph Backdoor Attack","date":"2022-06-21","arxiv_id":"2207.00425","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-capsules-a-survey","title":"Learning with Capsules: A Survey","date":"2022-06-06","arxiv_id":"2206.02664","repositories_listed":0,"syntology":null},{"url":null,"slug":"omni-granular-ego-semantic-propagation-for","title":"Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning","date":"2022-05-31","arxiv_id":"2205.15746","repositories_listed":0,"syntology":null},{"url":null,"slug":"graphpmu-event-clustering-via-graph","title":"GraphPMU: Event Clustering via Graph Representation Learning Using Locationally-Scarce Distribution-Level Fundamental and Harmonic PMU Measurements","date":"2022-05-26","arxiv_id":"2205.13116","repositories_listed":0,"syntology":null},{"url":null,"slug":"kqgc-knowledge-graph-embedding-with-smoothing","title":"KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation","date":"2022-05-23","arxiv_id":"2205.12102","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-heterophilious-edge-to-drop-a","title":"Revisiting the role of heterophily in graph representation learning: An edge classification perspective","date":"2022-05-23","arxiv_id":"2205.11322","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-graph-representation-learning-methods","title":"Are Graph Representation Learning Methods Robust to Graph Sparsity and Asymmetric Node Information?","date":"2022-05-19","arxiv_id":"2205.09648","repositories_listed":0,"syntology":null},{"url":null,"slug":"poincare-heterogeneous-graph-neural-networks","title":"Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation","date":"2022-05-16","arxiv_id":"2205.11233","repositories_listed":0,"syntology":null},{"url":null,"slug":"embodied-symbolic-contrastive-graph-self","title":"Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs","date":"2022-05-13","arxiv_id":"2205.06783","repositories_listed":0,"syntology":null},{"url":null,"slug":"liftpool-lifting-based-graph-pooling-for","title":"LiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning","date":"2022-04-27","arxiv_id":"2204.12881","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-mapping-in-heterogeneous-systems","title":"End-to-end Mapping in Heterogeneous Systems Using Graph Representation Learning","date":"2022-04-25","arxiv_id":"2204.11981","repositories_listed":0,"syntology":null},{"url":null,"slug":"all-optical-graph-representation-learning","title":"All-optical graph representation learning using integrated diffractive photonic computing units","date":"2022-04-23","arxiv_id":"2204.10978","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-graph-representation-learning","title":"A Survey on Graph Representation Learning Methods","date":"2022-04-04","arxiv_id":"2204.01855","repositories_listed":0,"syntology":null},{"url":null,"slug":"graphcoco-graph-complementary-contrastive","title":"On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach","date":"2022-03-24","arxiv_id":"2203.12821","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainability-in-graph-neural-networks-an","title":"Explainability in Graph Neural Networks: An Experimental Survey","date":"2022-03-17","arxiv_id":"2203.09258","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-learning-on-graphs-a-survey","title":"Few-Shot Learning on Graphs","date":"2022-03-17","arxiv_id":"2203.09308","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-representation-learning-with","title":"Graph Representation Learning with Individualization and Refinement","date":"2022-03-17","arxiv_id":"2203.09141","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-representation-learning-for-popularity","title":"Graph Representation Learning for Popularity Prediction Problem: A Survey","date":"2022-03-15","arxiv_id":"2203.07632","repositories_listed":0,"syntology":null},{"url":null,"slug":"flurry-a-fast-framework-for-reproducible","title":"Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning","date":"2022-03-05","arxiv_id":"2203.02744","repositories_listed":0,"syntology":null},{"url":null,"slug":"distribution-preserving-graph-representation","title":"Distribution Preserving Graph Representation Learning","date":"2022-02-27","arxiv_id":"2202.13428","repositories_listed":0,"syntology":null},{"url":null,"slug":"message-passing-all-the-way-up","title":"Message passing all the way up","date":"2022-02-22","arxiv_id":"2202.11097","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-visual-pattern-search-on-graph","title":"Interactive Visual Pattern Search on Graph Data via Graph Representation Learning","date":"2022-02-18","arxiv_id":"2202.09459","repositories_listed":0,"syntology":null},{"url":null,"slug":"geometric-graph-representation-learning-via","title":"Geometric Graph Representation Learning via Maximizing Rate Reduction","date":"2022-02-13","arxiv_id":"2202.06241","repositories_listed":0,"syntology":null},{"url":null,"slug":"urban-region-profiling-via-a-multi-graph","title":"Urban Region Profiling via A Multi-Graph Representation Learning Framework","date":"2022-02-04","arxiv_id":"2202.02074","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-large-scale-heterogeneous-graph","title":"Using Large-scale Heterogeneous Graph Representation Learning for Code Review Recommendations at Microsoft","date":"2022-02-04","arxiv_id":"2202.02385","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-robust-representation-through-graph","title":"Learning Robust Representation through Graph Adversarial Contrastive Learning","date":"2022-01-31","arxiv_id":"2201.13025","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-learning-of-hierarchical-community","title":"Joint Learning of Hierarchical Community Structure and Node Representations: An Unsupervised Approach","date":"2022-01-22","arxiv_id":"2201.09086","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-hyperbolic-graph-embeddings-via","title":"Enhancing Hyperbolic Graph Embeddings via Contrastive Learning","date":"2022-01-21","arxiv_id":"2201.08554","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-unsupervised-graph-representation","title":"Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective","date":"2022-01-21","arxiv_id":"2201.08557","repositories_listed":0,"syntology":null}],"record_sha256":"fe7310441dae75d4097acca170e542404c32e52fef4166ecf655e59818070d65","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}