{"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-neural-network/papers/12","list_of":"/method/graph-neural-network","method":"Graph Neural Network","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":12,"pages_in_order":27,"rows_per_page":100,"rows":[1101,1200],"of":2694,"counts":{"archive_papers_tagged":2694,"with_a_code_link":1137,"where_syntology_ran_a_sample":271,"not_listed_spam_title":0,"listed":2694,"listed_where_code_ran":271,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":235,"every_run_a_failure_of_syntologys_instrument":36,"listed_with_a_run_with_no_instrument_failure":235,"listed_every_run_a_failure_of_syntologys_instrument":36,"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-neural-network","prev":"/method/graph-neural-network/papers/11","next":"/method/graph-neural-network/papers/13","papers":[{"paper":null,"slug":"barriers-for-the-performance-of-graph-neural","title":"Barriers for the performance of graph neural networks (GNN) in discrete random structures. A comment on~\\cite{schuetz2022combinatorial},\\cite{angelini2023modern},\\cite{schuetz2023reply}","date":"2023-06-05","arxiv_id":"2306.02555","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-similarity-among-users-for","title":"Learning Similarity among Users for Personalized Session-Based Recommendation from hierarchical structure of User-Session-Item","date":"2023-06-05","arxiv_id":"2306.03040","n_code_links":0,"syntology":null},{"paper":"/paper/structural-re-weighting-improves-graph-domain","slug":"structural-re-weighting-improves-graph-domain","title":"Structural Re-weighting Improves Graph Domain Adaptation","date":"2023-06-05","arxiv_id":"2306.03221","n_code_links":1,"syntology":{"ran":2,"of":7,"n_ran_checked":1,"n_instrument":1,"unverified":5,"pointer_only":7,"phrase":"2 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; 1 where Syntology's instrument failed) · 5 unverified","official":{"repos":["graph-com/strurw"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"recent-advances-in-graph-based-machine","title":"Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems","date":"2023-06-02","arxiv_id":"2306.01282","n_code_links":0,"syntology":null},{"paper":null,"slug":"interpreting-gnn-based-ids-detections-using","title":"Interpreting GNN-based IDS Detections Using Provenance Graph Structural Features","date":"2023-06-01","arxiv_id":"2306.00934","n_code_links":0,"syntology":null},{"paper":"/paper/reconstructing-graph-diffusion-history-from-a","slug":"reconstructing-graph-diffusion-history-from-a","title":"Reconstructing Graph Diffusion History from a Single Snapshot","date":"2023-06-01","arxiv_id":"2306.00488","n_code_links":1,"syntology":{"ran":2,"of":11,"n_ran_checked":2,"n_instrument":0,"unverified":9,"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) · 9 unverified","official":{"repos":["q-rz/kdd23-ditto"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":9,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"catalysis-distillation-neural-network-for-the","title":"Catalysis distillation neural network for the few shot open catalyst challenge","date":"2023-05-31","arxiv_id":"2305.19545","n_code_links":0,"syntology":null},{"paper":null,"slug":"contrastive-hierarchical-discourse-graph-for","title":"Contrastive Hierarchical Discourse Graph for Scientific Document Summarization","date":"2023-05-31","arxiv_id":"2306.00177","n_code_links":0,"syntology":null},{"paper":"/paper/explanations-as-features-llm-based-features","slug":"explanations-as-features-llm-based-features","title":"Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning","date":"2023-05-31","arxiv_id":"2305.19523","n_code_links":3,"syntology":{"ran":6,"of":7,"n_ran_checked":6,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["XiaoxinHe/TAPE","xiaoxinhe/tape_arxiv_2023"],"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":"towards-semi-supervised-universal-graph","title":"Towards Semi-supervised Universal Graph Classification","date":"2023-05-31","arxiv_id":"2305.19598","n_code_links":0,"syntology":null},{"paper":null,"slug":"verifying-the-smoothness-of-graph-signals-a","title":"Verifying the Smoothness of Graph Signals: A Graph Signal Processing Approach","date":"2023-05-31","arxiv_id":"2305.19618","n_code_links":0,"syntology":null},{"paper":"/paper/graph-neural-networks-for-contextual-asr-with","slug":"graph-neural-networks-for-contextual-asr-with","title":"Graph Neural Networks for Contextual ASR with the Tree-Constrained Pointer Generator","date":"2023-05-30","arxiv_id":"2305.18824","n_code_links":1,"syntology":null},{"paper":null,"slug":"convolutional-neural-network-based-on-sparse","title":"Convolutional neural network based on sparse graph attention mechanism for MRI super-resolution","date":"2023-05-29","arxiv_id":"2305.17898","n_code_links":0,"syntology":null},{"paper":"/paper/gazegnn-a-gaze-guided-graph-neural-network","slug":"gazegnn-a-gaze-guided-graph-neural-network","title":"GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification","date":"2023-05-29","arxiv_id":"2305.18221","n_code_links":1,"syntology":null},{"paper":null,"slug":"geometric-graph-filters-and-neural-networks","title":"Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offs","date":"2023-05-29","arxiv_id":"2305.18467","n_code_links":0,"syntology":null},{"paper":"/paper/exploiting-abstract-meaning-representation","slug":"exploiting-abstract-meaning-representation","title":"Exploiting Abstract Meaning Representation for Open-Domain Question Answering","date":"2023-05-26","arxiv_id":"2305.17050","n_code_links":1,"syntology":null},{"paper":"/paper/gvdoc-graph-based-visual-document","slug":"gvdoc-graph-based-visual-document","title":"GVdoc: Graph-based Visual Document Classification","date":"2023-05-26","arxiv_id":"2305.17219","n_code_links":1,"syntology":null},{"paper":"/paper/deepgate2-functionality-aware-circuit","slug":"deepgate2-functionality-aware-circuit","title":"DeepGate2: Functionality-Aware Circuit Representation Learning","date":"2023-05-25","arxiv_id":"2305.16373","n_code_links":1,"syntology":null},{"paper":"/paper/neural-incomplete-factorization-learning","slug":"neural-incomplete-factorization-learning","title":"Neural incomplete factorization: learning preconditioners for the conjugate gradient method","date":"2023-05-25","arxiv_id":"2305.16368","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["paulhausner/neural-incomplete-factorization"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"revisiting-generalized-p-laplacian","title":"Revisiting Generalized p-Laplacian Regularized Framelet GCNs: Convergence, Energy Dynamic and Training with Non-Linear Diffusion","date":"2023-05-25","arxiv_id":"2305.15639","n_code_links":0,"syntology":null},{"paper":"/paper/2305-14749","slug":"2305-14749","title":"gRNAde: Geometric Deep Learning for 3D RNA inverse design","date":"2023-05-24","arxiv_id":"2305.14749","n_code_links":3,"syntology":{"ran":17,"of":29,"n_ran_checked":4,"n_instrument":13,"unverified":12,"pointer_only":0,"phrase":"17 ran (of which 1 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 2 violated, 1 with no contract checked; 13 where Syntology's instrument failed) · 12 unverified","official":{"repos":["chaitjo/geometric-rna-design"],"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":["listed","official"]}}},{"paper":"/paper/reversible-graph-neural-network-based","slug":"reversible-graph-neural-network-based","title":"Reversible Graph Neural Network-based Reaction Distribution Learning for Multiple Appropriate Facial Reactions Generation","date":"2023-05-24","arxiv_id":"2305.15270","n_code_links":1,"syntology":null},{"paper":"/paper/stochastic-unrolled-federated-learning","slug":"stochastic-unrolled-federated-learning","title":"Stochastic Unrolled Federated Learning","date":"2023-05-24","arxiv_id":"2305.15371","n_code_links":1,"syntology":null},{"paper":"/paper/towards-few-shot-entity-recognition-in-2","slug":"towards-few-shot-entity-recognition-in-2","title":"Towards Few-shot Entity Recognition in Document Images: A Graph Neural Network Approach Robust to Image Manipulation","date":"2023-05-24","arxiv_id":"2305.14828","n_code_links":1,"syntology":null},{"paper":"/paper/2305-14562","slug":"2305-14562","title":"GiPH: Generalizable Placement Learning for Adaptive Heterogeneous Computing","date":"2023-05-23","arxiv_id":"2305.14562","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-structural-expressive-power-of-graph","title":"On Structural Expressive Power of Graph Transformers","date":"2023-05-23","arxiv_id":"2305.13987","n_code_links":0,"syntology":null},{"paper":null,"slug":"sequential-graph-neural-networks-for-source","title":"Sequential Graph Neural Networks for Source Code Vulnerability Identification","date":"2023-05-23","arxiv_id":"2306.05375","n_code_links":0,"syntology":null},{"paper":null,"slug":"gatology-for-linguistics-what-syntactic","title":"GATology for Linguistics: What Syntactic Dependencies It Knows","date":"2023-05-22","arxiv_id":"2305.13403","n_code_links":0,"syntology":null},{"paper":null,"slug":"graphcare-enhancing-healthcare-predictions","title":"GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs","date":"2023-05-22","arxiv_id":"2305.12788","n_code_links":0,"syntology":null},{"paper":"/paper/instant-representation-learning-for","slug":"instant-representation-learning-for","title":"Instant Representation Learning for Recommendation over Large Dynamic Graphs","date":"2023-05-22","arxiv_id":"2305.18622","n_code_links":1,"syntology":null},{"paper":"/paper/multi-behavior-self-supervised-learning-for","slug":"multi-behavior-self-supervised-learning-for","title":"Multi-behavior Self-supervised Learning for Recommendation","date":"2023-05-22","arxiv_id":"2305.18238","n_code_links":1,"syntology":null},{"paper":"/paper/road-planning-for-slums-via-deep","slug":"road-planning-for-slums-via-deep","title":"Road Planning for Slums via Deep Reinforcement Learning","date":"2023-05-22","arxiv_id":"2305.13060","n_code_links":1,"syntology":null},{"paper":null,"slug":"prodigy-enabling-in-context-learning-over","title":"PRODIGY: Enabling In-context Learning Over Graphs","date":"2023-05-21","arxiv_id":"2305.12600","n_code_links":0,"syntology":null},{"paper":null,"slug":"domain-generalization-deep-graph","title":"Domain Generalization Deep Graph Transformation","date":"2023-05-19","arxiv_id":"2305.11389","n_code_links":0,"syntology":null},{"paper":"/paper/graphfc-customs-fraud-detection-with-label","slug":"graphfc-customs-fraud-detection-with-label","title":"GraphFC: Customs Fraud Detection with Label Scarcity","date":"2023-05-19","arxiv_id":"2305.11377","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-for-open-world-calibration-with","title":"Learning for Transductive Threshold Calibration in Open-World Recognition","date":"2023-05-19","arxiv_id":"2305.12039","n_code_links":0,"syntology":null},{"paper":null,"slug":"chainlet-orbits-topological-address-embedding","title":"Chainlet Orbits: Topological Address Embedding for the Bitcoin Blockchain","date":"2023-05-18","arxiv_id":"2306.07974","n_code_links":0,"syntology":null},{"paper":null,"slug":"drugchat-towards-enabling-chatgpt-like","title":"DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule Graphs","date":"2023-05-18","arxiv_id":"2309.03907","n_code_links":0,"syntology":null},{"paper":"/paper/seq-hgnn-learning-sequential-node","slug":"seq-hgnn-learning-sequential-node","title":"Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph","date":"2023-05-18","arxiv_id":"2305.10771","n_code_links":1,"syntology":null},{"paper":null,"slug":"curriculum-learning-in-job-shop-scheduling","title":"Curriculum Learning in Job Shop Scheduling using Reinforcement Learning","date":"2023-05-17","arxiv_id":"2305.10192","n_code_links":0,"syntology":null},{"paper":"/paper/edge-directionality-improves-learning-on","slug":"edge-directionality-improves-learning-on","title":"Edge Directionality Improves Learning on Heterophilic Graphs","date":"2023-05-17","arxiv_id":"2305.10498","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":4,"n_instrument":0,"unverified":1,"pointer_only":4,"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":["emalgorithm/directed-graph-neural-network"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"optimality-of-message-passing-architectures","title":"Optimality of Message-Passing Architectures for Sparse Graphs","date":"2023-05-17","arxiv_id":"2305.10391","n_code_links":0,"syntology":null},{"paper":"/paper/predicting-tweet-engagement-with-graph-neural","slug":"predicting-tweet-engagement-with-graph-neural","title":"Predicting Tweet Engagement with Graph Neural Networks","date":"2023-05-17","arxiv_id":"2305.10103","n_code_links":1,"syntology":null},{"paper":"/paper/dynamic-causal-explanation-based-diffusion","slug":"dynamic-causal-explanation-based-diffusion","title":"Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting","date":"2023-05-16","arxiv_id":"2305.09703","n_code_links":1,"syntology":null},{"paper":"/paper/enhancing-keyphrase-extraction-from-long","slug":"enhancing-keyphrase-extraction-from-long","title":"Enhancing Keyphrase Extraction from Long Scientific Documents using Graph Embeddings","date":"2023-05-16","arxiv_id":"2305.09316","n_code_links":1,"syntology":null},{"paper":null,"slug":"inductive-graph-neural-networks-for-moving","title":"Inductive Graph Neural Networks for Moving Object Segmentation","date":"2023-05-16","arxiv_id":"2305.09585","n_code_links":0,"syntology":null},{"paper":"/paper/decoupled-graph-neural-networks-for-large","slug":"decoupled-graph-neural-networks-for-large","title":"Decoupled Graph Neural Networks for Large Dynamic Graphs","date":"2023-05-14","arxiv_id":"2305.08273","n_code_links":1,"syntology":null},{"paper":"/paper/graph-neural-networks-based-user-pairing-in","slug":"graph-neural-networks-based-user-pairing-in","title":"Graph Neural Networks-Based User Pairing in Wireless Communication Systems","date":"2023-05-14","arxiv_id":"2306.00717","n_code_links":1,"syntology":null},{"paper":null,"slug":"knowledge-soft-integration-for-multimodal","title":"Knowledge Soft Integration for Multimodal Recommendation","date":"2023-05-12","arxiv_id":"2305.07419","n_code_links":0,"syntology":null},{"paper":null,"slug":"graph-neural-network-for-accurate-and-low","title":"Graph Neural Network for Accurate and Low-complexity SAR ATR","date":"2023-05-11","arxiv_id":"2305.07119","n_code_links":0,"syntology":null},{"paper":null,"slug":"how-expressive-are-spectral-temporal-graph","title":"How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting?","date":"2023-05-11","arxiv_id":"2305.06587","n_code_links":0,"syntology":null},{"paper":"/paper/cuts-high-dimensional-causal-discovery-from","slug":"cuts-high-dimensional-causal-discovery-from","title":"CUTS+: High-dimensional Causal Discovery from Irregular Time-series","date":"2023-05-10","arxiv_id":"2305.05890","n_code_links":1,"syntology":{"ran":8,"of":8,"n_ran_checked":2,"n_instrument":6,"unverified":0,"pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 6 where Syntology's instrument failed) · 0 unverified","official":{"repos":["jarrycyx/unn"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/dual-intent-enhanced-graph-neural-network-for","slug":"dual-intent-enhanced-graph-neural-network-for","title":"Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation","date":"2023-05-10","arxiv_id":"2305.05848","n_code_links":1,"syntology":null},{"paper":null,"slug":"graph-neural-network-interatomic-potential","title":"Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces","date":"2023-05-10","arxiv_id":"2305.16325","n_code_links":0,"syntology":null},{"paper":null,"slug":"cooperating-graph-neural-networks-with-deep","title":"Cooperating Graph Neural Networks with Deep Reinforcement Learning for Vaccine Prioritization","date":"2023-05-09","arxiv_id":"2305.05163","n_code_links":0,"syntology":null},{"paper":"/paper/temporal-and-heterogeneous-graph-neural","slug":"temporal-and-heterogeneous-graph-neural","title":"Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction","date":"2023-05-09","arxiv_id":"2305.08740","n_code_links":2,"syntology":null},{"paper":"/paper/traffic-forecasting-on-new-roads-unseen-in","slug":"traffic-forecasting-on-new-roads-unseen-in","title":"Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT)","date":"2023-05-09","arxiv_id":"2305.05237","n_code_links":1,"syntology":null},{"paper":null,"slug":"can-graph-neural-network-based-detection","title":"Can graph neural network-based detection mitigate the impact of hardware imperfections?","date":"2023-05-08","arxiv_id":"2305.04612","n_code_links":0,"syntology":null},{"paper":"/paper/language-independent-neuro-symbolic-semantic","slug":"language-independent-neuro-symbolic-semantic","title":"Language Independent Neuro-Symbolic Semantic Parsing for Form Understanding","date":"2023-05-08","arxiv_id":"2305.04460","n_code_links":1,"syntology":null},{"paper":"/paper/heterogeneous-directed-hypergraph-neural","slug":"heterogeneous-directed-hypergraph-neural","title":"Heterogeneous Directed Hypergraph Neural Network over abstract syntax tree (AST) for Code Classification","date":"2023-05-07","arxiv_id":"2305.04228","n_code_links":1,"syntology":null},{"paper":null,"slug":"hiore-leveraging-high-order-interactions-for","title":"HIORE: Leveraging High-order Interactions for Unified Entity Relation Extraction","date":"2023-05-07","arxiv_id":"2305.04297","n_code_links":0,"syntology":null},{"paper":"/paper/lsgnn-towards-general-graph-neural-network-in","slug":"lsgnn-towards-general-graph-neural-network-in","title":"LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity","date":"2023-05-07","arxiv_id":"2305.04225","n_code_links":1,"syntology":null},{"paper":"/paper/conversational-semantic-parsing-using-dynamic","slug":"conversational-semantic-parsing-using-dynamic","title":"Conversational Semantic Parsing using Dynamic Context Graphs","date":"2023-05-04","arxiv_id":"2305.06164","n_code_links":1,"syntology":null},{"paper":"/paper/transforming-visual-scene-graphs-to-image","slug":"transforming-visual-scene-graphs-to-image","title":"Transforming Visual Scene Graphs to Image Captions","date":"2023-05-03","arxiv_id":"2305.02177","n_code_links":1,"syntology":null},{"paper":null,"slug":"analysis-of-different-temporal-graph-neural","title":"Analysis of different temporal graph neural network configurations on dynamic graphs","date":"2023-05-02","arxiv_id":"2305.01128","n_code_links":0,"syntology":null},{"paper":"/paper/learning-controllable-adaptive-simulation-for","slug":"learning-controllable-adaptive-simulation-for","title":"Learning Controllable Adaptive Simulation for Multi-resolution Physics","date":"2023-05-01","arxiv_id":"2305.01122","n_code_links":1,"syntology":{"ran":5,"of":11,"n_ran_checked":4,"n_instrument":1,"unverified":6,"pointer_only":1,"phrase":"5 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; 1 where Syntology's instrument failed) · 6 unverified","official":{"repos":["snap-stanford/lamp"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"collective-relational-inference-for-learning","title":"Collective Relational Inference for learning heterogeneous interactions","date":"2023-04-30","arxiv_id":"2305.00557","n_code_links":0,"syntology":null},{"paper":"/paper/nearly-optimal-steiner-trees-using-graph","slug":"nearly-optimal-steiner-trees-using-graph","title":"Nearly Optimal Steiner Trees using Graph Neural Network Assisted Monte Carlo Tree Search","date":"2023-04-30","arxiv_id":"2305.00535","n_code_links":1,"syntology":null},{"paper":null,"slug":"physics-guided-graph-neural-networks-for-real","title":"Physics-Guided Graph Neural Networks for Real-time AC/DC Power Flow Analysis","date":"2023-04-29","arxiv_id":"2305.00216","n_code_links":0,"syntology":null},{"paper":"/paper/musical-voice-separation-as-link-prediction","slug":"musical-voice-separation-as-link-prediction","title":"Musical Voice Separation as Link Prediction: Modeling a Musical Perception Task as a Multi-Trajectory Tracking Problem","date":"2023-04-28","arxiv_id":"2304.14848","n_code_links":1,"syntology":null},{"paper":"/paper/x-rlflow-graph-reinforcement-learning-for","slug":"x-rlflow-graph-reinforcement-learning-for","title":"X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs Transformation","date":"2023-04-28","arxiv_id":"2304.14698","n_code_links":1,"syntology":null},{"paper":null,"slug":"graph-covis-gnn-based-multi-view-panorama","title":"Graph-CoVis: GNN-based Multi-view Panorama Global Pose Estimation","date":"2023-04-26","arxiv_id":"2304.13201","n_code_links":0,"syntology":null},{"paper":null,"slug":"node-feature-augmentation-vitaminizes-network","title":"Centrality-Based Node Feature Augmentation for Robust Network Alignment","date":"2023-04-25","arxiv_id":"2304.12751","n_code_links":0,"syntology":null},{"paper":null,"slug":"performance-optimization-using-multimodal","title":"Performance Optimization using Multimodal Modeling and Heterogeneous GNN","date":"2023-04-25","arxiv_id":"2304.12568","n_code_links":0,"syntology":null},{"paper":"/paper/impact-oriented-contextual-scholar-profiling","slug":"impact-oriented-contextual-scholar-profiling","title":"Impact-Oriented Contextual Scholar Profiling using Self-Citation Graphs","date":"2023-04-24","arxiv_id":"2304.12217","n_code_links":1,"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":["visdata/geneticflow"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/paragraph2graph-a-gnn-based-framework-for","slug":"paragraph2graph-a-gnn-based-framework-for","title":"PARAGRAPH2GRAPH: A GNN-based framework for layout paragraph analysis","date":"2023-04-24","arxiv_id":"2304.11810","n_code_links":1,"syntology":null},{"paper":null,"slug":"tgnn-a-joint-semi-supervised-framework-for","title":"TGNN: A Joint Semi-supervised Framework for Graph-level Classification","date":"2023-04-23","arxiv_id":"2304.11688","n_code_links":0,"syntology":null},{"paper":null,"slug":"detecting-political-opinions-in-tweets","title":"Detecting Political Opinions in Tweets through Bipartite Graph Analysis: A Skip Aggregation Graph Convolution Approach","date":"2023-04-22","arxiv_id":"2304.11367","n_code_links":0,"syntology":null},{"paper":"/paper/detecting-spoilers-in-movie-reviews-with","slug":"detecting-spoilers-in-movie-reviews-with","title":"Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks","date":"2023-04-22","arxiv_id":"2304.11411","n_code_links":1,"syntology":{"ran":3,"of":7,"n_ran_checked":3,"n_instrument":0,"unverified":4,"pointer_only":7,"phrase":"3 ran (of which 3 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) · 4 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","official":{"repos":["arthur-heng/spoiler-detection"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"medium-permeation-sars-cov-2-painting","title":"Medium. Permeation: SARS-COV-2 Painting Creation by Generative Model","date":"2023-04-22","arxiv_id":"2304.11354","n_code_links":0,"syntology":null},{"paper":"/paper/graph-neural-network-based-anomaly-detection-1","slug":"graph-neural-network-based-anomaly-detection-1","title":"Graph Neural Network-Based Anomaly Detection for River Network Systems","date":"2023-04-19","arxiv_id":"2304.09367","n_code_links":1,"syntology":null},{"paper":null,"slug":"solving-the-kidney-exchange-problem-via-graph","title":"Solving the Kidney-Exchange Problem via Graph Neural Networks with No Supervision","date":"2023-04-19","arxiv_id":"2304.09975","n_code_links":0,"syntology":null},{"paper":null,"slug":"neural-networks-for-geospatial-data","title":"Neural networks for geospatial data","date":"2023-04-18","arxiv_id":"2304.09157","n_code_links":0,"syntology":null},{"paper":null,"slug":"nps-a-framework-for-accurate-program-sampling","title":"NPS: A Framework for Accurate Program Sampling Using Graph Neural Network","date":"2023-04-18","arxiv_id":"2304.08880","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-to-rank-resources-with-gnn","title":"Learning To Rank Resources with GNN","date":"2023-04-17","arxiv_id":"2304.07946","n_code_links":0,"syntology":null},{"paper":"/paper/normalizing-flow-based-neural-process-for-few","slug":"normalizing-flow-based-neural-process-for-few","title":"Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion","date":"2023-04-17","arxiv_id":"2304.08183","n_code_links":1,"syntology":null},{"paper":"/paper/m2gnn-metapath-and-multi-interest-aggregated","slug":"m2gnn-metapath-and-multi-interest-aggregated","title":"M2GNN: Metapath and Multi-interest Aggregated Graph Neural Network for Tag-based Cross-domain Recommendation","date":"2023-04-16","arxiv_id":"2304.07911","n_code_links":1,"syntology":null},{"paper":"/paper/hgwavenet-a-hyperbolic-graph-neural-network","slug":"hgwavenet-a-hyperbolic-graph-neural-network","title":"HGWaveNet: A Hyperbolic Graph Neural Network for Temporal Link Prediction","date":"2023-04-14","arxiv_id":"2304.07302","n_code_links":1,"syntology":null},{"paper":null,"slug":"ks-gnnexplainer-global-model-interpretation","title":"KS-GNNExplainer: Global Model Interpretation Through Instance Explanations On Histopathology images","date":"2023-04-14","arxiv_id":"2304.08240","n_code_links":0,"syntology":null},{"paper":"/paper/rf-gnn-random-forest-boosted-graph-neural","slug":"rf-gnn-random-forest-boosted-graph-neural","title":"RF-GNN: Random Forest Boosted Graph Neural Network for Social Bot Detection","date":"2023-04-14","arxiv_id":"2304.08239","n_code_links":1,"syntology":null},{"paper":"/paper/radargnn-transformation-invariant-graph","slug":"radargnn-transformation-invariant-graph","title":"RadarGNN: Transformation Invariant Graph Neural Network for Radar-based Perception","date":"2023-04-13","arxiv_id":"2304.06547","n_code_links":1,"syntology":null},{"paper":null,"slug":"road-network-representation-learning-a-dual","title":"Road Network Representation Learning: A Dual Graph based Approach","date":"2023-04-13","arxiv_id":"2304.07298","n_code_links":0,"syntology":null},{"paper":null,"slug":"dynamic-graph-representation-learning-with-1","title":"Dynamic Graph Representation Learning with Neural Networks: A Survey","date":"2023-04-12","arxiv_id":"2304.05729","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-to-communicate-and-collaborate-in-a","title":"Learning to Communicate and Collaborate in a Competitive Multi-Agent Setup to Clean the Ocean from Macroplastics","date":"2023-04-12","arxiv_id":"2304.05872","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comprehensive-survey-on-deep-graph","title":"A Comprehensive Survey on Deep Graph Representation Learning","date":"2023-04-11","arxiv_id":"2304.05055","n_code_links":0,"syntology":null},{"paper":"/paper/differentiable-graph-structured-models-for","slug":"differentiable-graph-structured-models-for","title":"Differentiable graph-structured models for inverse design of lattice materials","date":"2023-04-11","arxiv_id":"2304.05422","n_code_links":1,"syntology":null},{"paper":"/paper/todynet-temporal-dynamic-graph-neural-network","slug":"todynet-temporal-dynamic-graph-neural-network","title":"TodyNet: Temporal Dynamic Graph Neural Network for Multivariate Time Series Classification","date":"2023-04-11","arxiv_id":"2304.05078","n_code_links":1,"syntology":null},{"paper":"/paper/topology-reasoning-for-driving-scenes","slug":"topology-reasoning-for-driving-scenes","title":"Graph-based Topology Reasoning for Driving Scenes","date":"2023-04-11","arxiv_id":"2304.05277","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":["opendrivelab/toponet"],"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/contrastive-cross-domain-sequential","slug":"contrastive-cross-domain-sequential","title":"Contrastive Cross-Domain Sequential Recommendation","date":"2023-04-08","arxiv_id":"2304.03891","n_code_links":1,"syntology":null},{"paper":"/paper/generating-a-graph-colouring-heuristic-with","slug":"generating-a-graph-colouring-heuristic-with","title":"Generating a Graph Colouring Heuristic with Deep Q-Learning and Graph Neural Networks","date":"2023-04-08","arxiv_id":"2304.04051","n_code_links":1,"syntology":null}],"record_sha256":"e2c3b98ae48835013cd196b2af9b763fa483aaa0326b5765c578300845054a73","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}