{"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/8","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":8,"pages_in_order":27,"rows_per_page":100,"rows":[701,800],"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/7","next":"/method/graph-neural-network/papers/9","papers":[{"paper":null,"slug":"dual-channel-multiplex-graph-neural-networks","title":"Dual-Channel Multiplex Graph Neural Networks for Recommendation","date":"2024-03-18","arxiv_id":"2403.11624","n_code_links":0,"syntology":null},{"paper":null,"slug":"nugraph2-a-graph-neural-network-for-neutrino","title":"Graph Neural Network for Neutrino Physics Event Reconstruction","date":"2024-03-18","arxiv_id":"2403.11872","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-relational-graph-neural-network-for-out","title":"Multi-Relational Graph Neural Network for Out-of-Domain Link Prediction","date":"2024-03-17","arxiv_id":"2403.11292","n_code_links":0,"syntology":null},{"paper":null,"slug":"ecrc-emotion-causality-recognition-in-korean","title":"ECRC: Emotion-Causality Recognition in Korean Conversation for GCN","date":"2024-03-16","arxiv_id":"2403.10764","n_code_links":0,"syntology":null},{"paper":null,"slug":"discovering-invariant-neighborhood-patterns","title":"Discovering Invariant Neighborhood Patterns for Heterophilic Graphs","date":"2024-03-15","arxiv_id":"2403.10572","n_code_links":0,"syntology":null},{"paper":"/paper/from-chaos-to-clarity-time-series-anomaly","slug":"from-chaos-to-clarity-time-series-anomaly","title":"From Chaos to Clarity: Time Series Anomaly Detection in Astronomical Observations","date":"2024-03-15","arxiv_id":"2403.10220","n_code_links":1,"syntology":null},{"paper":null,"slug":"generation-is-better-than-modification","title":"Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection","date":"2024-03-15","arxiv_id":"2403.10339","n_code_links":0,"syntology":null},{"paper":null,"slug":"ensemble-quadratic-assignment-network-for","title":"Ensemble Quadratic Assignment Network for Graph Matching","date":"2024-03-11","arxiv_id":"2403.06457","n_code_links":0,"syntology":null},{"paper":null,"slug":"financial-default-prediction-via-motif","title":"Financial Default Prediction via Motif-preserving Graph Neural Network with Curriculum Learning","date":"2024-03-11","arxiv_id":"2403.06482","n_code_links":0,"syntology":null},{"paper":null,"slug":"signn-a-spike-induced-graph-neural-network","title":"SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning","date":"2024-03-11","arxiv_id":"2404.07941","n_code_links":0,"syntology":null},{"paper":"/paper/towards-a-generic-representation-of","slug":"towards-a-generic-representation-of","title":"Towards a Generic Representation of Combinatorial Problems for Learning-Based Approaches","date":"2024-03-09","arxiv_id":"2403.06026","n_code_links":1,"syntology":{"ran":11,"of":16,"n_ran_checked":11,"n_instrument":0,"unverified":5,"pointer_only":16,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","official":{"repos":["corail-research/learning-generic-csp"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":"/paper/jet-discrimination-with-quantum-complete","slug":"jet-discrimination-with-quantum-complete","title":"Jet Discrimination with Quantum Complete Graph Neural Network","date":"2024-03-08","arxiv_id":"2403.04990","n_code_links":1,"syntology":null},{"paper":"/paper/bloomgml-graph-machine-learning-through-the","slug":"bloomgml-graph-machine-learning-through-the","title":"BloomGML: Graph Machine Learning through the Lens of Bilevel Optimization","date":"2024-03-07","arxiv_id":"2403.04763","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-matrix-completion-by-exploiting","title":"Improving Matrix Completion by Exploiting Rating Ordinality in Graph Neural Networks","date":"2024-03-07","arxiv_id":"2403.04504","n_code_links":0,"syntology":null},{"paper":null,"slug":"uncertainty-aware-relational-graph-neural","title":"Uncertainty-Aware Relational Graph Neural Network for Few-Shot Knowledge Graph Completion","date":"2024-03-07","arxiv_id":"2403.04521","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-ai-enabled-agent-based-model-and-its","title":"An AI-enabled Agent-Based Model and Its Application in Measles Outbreak Simulation for New Zealand","date":"2024-03-06","arxiv_id":"2403.03434","n_code_links":0,"syntology":null},{"paper":null,"slug":"ldsf-lightweight-dual-stream-framework-for","title":"LDSF: Lightweight Dual-Stream Framework for SAR Target Recognition by Coupling Local Electromagnetic Scattering Features and Global Visual Features","date":"2024-03-06","arxiv_id":"2403.03527","n_code_links":0,"syntology":null},{"paper":null,"slug":"representation-learning-on-heterophilic-graph","title":"Representation Learning on Heterophilic Graph with Directional Neighborhood Attention","date":"2024-03-03","arxiv_id":"2403.01475","n_code_links":0,"syntology":null},{"paper":null,"slug":"cool-a-conjoint-perspective-on-spatio","title":"COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting","date":"2024-03-02","arxiv_id":"2403.01091","n_code_links":0,"syntology":null},{"paper":"/paper/subhomogeneous-deep-equilibrium-models","slug":"subhomogeneous-deep-equilibrium-models","title":"Subhomogeneous Deep Equilibrium Models","date":"2024-03-01","arxiv_id":"2403.00720","n_code_links":1,"syntology":{"ran":6,"of":8,"n_ran_checked":6,"n_instrument":0,"unverified":2,"pointer_only":8,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["COMPiLELab/SubDEQ"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"toward-autonomous-cooperation-in","title":"Toward Autonomous Cooperation in Heterogeneous Nanosatellite Constellations Using Dynamic Graph Neural Networks","date":"2024-03-01","arxiv_id":"2403.00692","n_code_links":0,"syntology":null},{"paper":null,"slug":"causal-graph-ode-continuous-treatment-effect","title":"Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems","date":"2024-02-29","arxiv_id":"2403.00178","n_code_links":0,"syntology":null},{"paper":"/paper/diffassemble-a-unified-graph-diffusion-model","slug":"diffassemble-a-unified-graph-diffusion-model","title":"DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly","date":"2024-02-29","arxiv_id":"2402.19302","n_code_links":1,"syntology":{"ran":8,"of":16,"n_ran_checked":7,"n_instrument":1,"unverified":8,"pointer_only":16,"phrase":"8 ran (of which 2 constructed an object rather than computing a result; 7 with no instrument failure: 3 honoured, 1 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 8 unverified","official":{"repos":["iit-pavis/diffassemble"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":2,"n_ran_no_instrument_failure":7,"n_unverified":8,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"graph-neural-networks-and-arithmetic-circuits","title":"Graph Neural Networks and Arithmetic Circuits","date":"2024-02-27","arxiv_id":"2402.17805","n_code_links":0,"syntology":null},{"paper":null,"slug":"prediction-of-the-sym-h-index-using-a","title":"Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification","date":"2024-02-27","arxiv_id":"2402.17196","n_code_links":0,"syntology":null},{"paper":null,"slug":"using-graph-neural-networks-to-predict-local","title":"Using Graph Neural Networks to Predict Local Culture","date":"2024-02-27","arxiv_id":"2402.17905","n_code_links":0,"syntology":null},{"paper":"/paper/accelerating-graph-neural-networks-on-real","slug":"accelerating-graph-neural-networks-on-real","title":"PyGim: An Efficient Graph Neural Network Library for Real Processing-In-Memory Architectures","date":"2024-02-26","arxiv_id":"2402.16731","n_code_links":1,"syntology":null},{"paper":null,"slug":"minimize-control-inputs-for-strong-structural","title":"Minimize Control Inputs for Strong Structural Controllability Using Reinforcement Learning with Graph Neural Network","date":"2024-02-26","arxiv_id":"2402.16925","n_code_links":0,"syntology":null},{"paper":null,"slug":"one-shot-graph-representation-learning-using","title":"Hyperdimensional Representation Learning for Node Classification and Link Prediction","date":"2024-02-26","arxiv_id":"2402.17073","n_code_links":0,"syntology":null},{"paper":"/paper/two-stage-generative-question-answering-on","slug":"two-stage-generative-question-answering-on","title":"Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models","date":"2024-02-26","arxiv_id":"2402.16568","n_code_links":0,"syntology":null},{"paper":null,"slug":"cat-gnn-enhancing-credit-card-fraud-detection","title":"CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks","date":"2024-02-22","arxiv_id":"2402.14708","n_code_links":0,"syntology":null},{"paper":null,"slug":"path-planning-based-on-2d-object-bounding-box","title":"Path Planning based on 2D Object Bounding-box","date":"2024-02-22","arxiv_id":"2402.14933","n_code_links":0,"syntology":null},{"paper":null,"slug":"attackgnn-red-teaming-gnns-in-hardware","title":"AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning","date":"2024-02-21","arxiv_id":"2402.13946","n_code_links":0,"syntology":null},{"paper":null,"slug":"hettree-heterogeneous-tree-graph-neural","title":"Heterogeneous Graph Neural Network on Semantic Tree","date":"2024-02-21","arxiv_id":"2402.13496","n_code_links":0,"syntology":null},{"paper":"/paper/linear-time-graph-neural-networks-for","slug":"linear-time-graph-neural-networks-for","title":"Linear-Time Graph Neural Networks for Scalable Recommendations","date":"2024-02-21","arxiv_id":"2402.13973","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["qwq2000/thewebconf24-ltgnn-pytorch"],"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":null,"slug":"vn-network-embedding-newly-emerging-entities","title":"VN Network: Embedding Newly Emerging Entities with Virtual Neighbors","date":"2024-02-21","arxiv_id":"2402.14033","n_code_links":0,"syntology":null},{"paper":"/paper/a-microstructure-based-graph-neural-network","slug":"a-microstructure-based-graph-neural-network","title":"A Microstructure-based Graph Neural Network for Accelerating Multiscale Simulations","date":"2024-02-20","arxiv_id":"2402.13101","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-unifying-primary-framework-for-quantum","title":"A unifying primary framework for quantum graph neural networks from quantum graph states","date":"2024-02-20","arxiv_id":"2402.13001","n_code_links":0,"syntology":null},{"paper":"/paper/can-gnn-be-good-adapter-for-llms","slug":"can-gnn-be-good-adapter-for-llms","title":"Can GNN be Good Adapter for LLMs?","date":"2024-02-20","arxiv_id":"2402.12984","n_code_links":2,"syntology":{"ran":6,"of":8,"n_ran_checked":6,"n_instrument":0,"unverified":2,"pointer_only":8,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["zjunet/graphadapter","hxttkl/GraphAdapter"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"partial-search-in-a-frozen-network-is-enough","title":"Partial Search in a Frozen Network is Enough to Find a Strong Lottery Ticket","date":"2024-02-20","arxiv_id":"2402.14029","n_code_links":0,"syntology":null},{"paper":null,"slug":"vehicle-group-based-crash-risk-formation-and","title":"Vehicle-group-based Crash Risk Prediction and Interpretation on Highways","date":"2024-02-19","arxiv_id":"2402.12415","n_code_links":0,"syntology":null},{"paper":"/paper/gnnavi-navigating-the-information-flow-in","slug":"gnnavi-navigating-the-information-flow-in","title":"GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network","date":"2024-02-18","arxiv_id":"2402.11709","n_code_links":1,"syntology":null},{"paper":"/paper/pascl-supervised-contrastive-learning-with","slug":"pascl-supervised-contrastive-learning-with","title":"PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction","date":"2024-02-18","arxiv_id":"2402.11538","n_code_links":1,"syntology":null},{"paper":"/paper/asgea-exploiting-logic-rules-from-align","slug":"asgea-exploiting-logic-rules-from-align","title":"ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment","date":"2024-02-16","arxiv_id":"2402.11000","n_code_links":2,"syntology":null},{"paper":null,"slug":"approximate-message-passing-enhanced-graph","title":"Approximate Message Passing-Enhanced Graph Neural Network for OTFS Data Detection","date":"2024-02-15","arxiv_id":"2402.10071","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-we-soft-prompt-llms-for-graph-learning","title":"Can we Soft Prompt LLMs for Graph Learning Tasks?","date":"2024-02-15","arxiv_id":"2402.10359","n_code_links":0,"syntology":null},{"paper":"/paper/chemreasoner-heuristic-search-over-a-large","slug":"chemreasoner-heuristic-search-over-a-large","title":"ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedback","date":"2024-02-15","arxiv_id":"2402.10980","n_code_links":1,"syntology":{"ran":5,"of":7,"n_ran_checked":0,"n_instrument":5,"unverified":2,"pointer_only":7,"phrase":"5 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; 5 where Syntology's instrument failed) · 2 unverified","official":{"repos":["pnnl/chemreasoner"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/improving-cognitive-diagnosis-models-with","slug":"improving-cognitive-diagnosis-models-with","title":"Exploring Heterogeneity and Uncertainty for Graph-based Cognitive Diagnosis Models in Intelligent Education","date":"2024-02-15","arxiv_id":"2403.05559","n_code_links":1,"syntology":null},{"paper":null,"slug":"uncertainty-aware-transient-stability","title":"Uncertainty-Aware Transient Stability-Constrained Preventive Redispatch: A Distributional Reinforcement Learning Approach","date":"2024-02-14","arxiv_id":"2402.09263","n_code_links":0,"syntology":null},{"paper":null,"slug":"graph-feature-preprocessor-real-time","title":"Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection","date":"2024-02-13","arxiv_id":"2402.08593","n_code_links":0,"syntology":null},{"paper":null,"slug":"modeling-balanced-explicit-and-implicit","title":"Modeling Balanced Explicit and Implicit Relations with Contrastive Learning for Knowledge Concept Recommendation in MOOCs","date":"2024-02-13","arxiv_id":"2402.08256","n_code_links":0,"syntology":null},{"paper":"/paper/parallel-friendly-spatio-temporal-graph","slug":"parallel-friendly-spatio-temporal-graph","title":"Parallel-friendly Spatio-Temporal Graph Learning for Photovoltaic Degradation Analysis at Scale","date":"2024-02-13","arxiv_id":"2402.08470","n_code_links":1,"syntology":null},{"paper":"/paper/netinfof-framework-measuring-and-exploiting","slug":"netinfof-framework-measuring-and-exploiting","title":"NetInfoF Framework: Measuring and Exploiting Network Usable Information","date":"2024-02-12","arxiv_id":"2402.07999","n_code_links":1,"syntology":{"ran":6,"of":8,"n_ran_checked":6,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["amazon-science/network-usable-info-framework"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"random-geometric-graph-alignment-with-graph","title":"Perfect Recovery for Random Geometric Graph Matching with Shallow Graph Neural Networks","date":"2024-02-12","arxiv_id":"2402.07340","n_code_links":0,"syntology":null},{"paper":"/paper/explainable-global-wildfire-prediction-models","slug":"explainable-global-wildfire-prediction-models","title":"Explainable Global Wildfire Prediction Models using Graph Neural Networks","date":"2024-02-11","arxiv_id":"2402.07152","n_code_links":1,"syntology":null},{"paper":null,"slug":"large-language-model-empowered-dose-volume","title":"Large-Language-Model Empowered Dose Volume Histogram Prediction for Intensity Modulated Radiotherapy","date":"2024-02-11","arxiv_id":"2402.07167","n_code_links":0,"syntology":null},{"paper":"/paper/core-gd-a-hierarchical-framework-for-scalable","slug":"core-gd-a-hierarchical-framework-for-scalable","title":"CoRe-GD: A Hierarchical Framework for Scalable Graph Visualization with GNNs","date":"2024-02-09","arxiv_id":"2402.06706","n_code_links":1,"syntology":{"ran":7,"of":10,"n_ran_checked":7,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["floriangroetschla/core-gd"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"n-1-reduced-optimal-power-flow-using","title":"N-1 Reduced Optimal Power Flow Using Augmented Hierarchical Graph Neural Network","date":"2024-02-09","arxiv_id":"2402.06226","n_code_links":0,"syntology":null},{"paper":"/paper/anfinsen-goes-neural-a-graphical-model-for","slug":"anfinsen-goes-neural-a-graphical-model-for","title":"Decoupled Sequence and Structure Generation for Realistic Antibody Design","date":"2024-02-08","arxiv_id":"2402.05982","n_code_links":1,"syntology":null},{"paper":"/paper/moco-a-learnable-meta-optimizer-for","slug":"moco-a-learnable-meta-optimizer-for","title":"Moco: A Learnable Meta Optimizer for Combinatorial Optimization","date":"2024-02-07","arxiv_id":"2402.04915","n_code_links":1,"syntology":{"ran":9,"of":10,"n_ran_checked":9,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["timd3/moco"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"link-prediction-with-relational-hypergraphs","title":"Link Prediction with Relational Hypergraphs","date":"2024-02-06","arxiv_id":"2402.04062","n_code_links":0,"syntology":null},{"paper":null,"slug":"single-gpu-gnn-systems-traps-and-pitfalls","title":"Single-GPU GNN Systems: Traps and Pitfalls","date":"2024-02-05","arxiv_id":"2402.03548","n_code_links":0,"syntology":null},{"paper":"/paper/a-comparative-analysis-of-gene-expression","slug":"a-comparative-analysis-of-gene-expression","title":"A Comparative Analysis of Gene Expression Profiling by Statistical and Machine Learning Approaches","date":"2024-02-01","arxiv_id":"2402.00926","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-novel-approach-for-detecting-deep-fake","title":"A novel approach for detecting deep fake videos using graph neural network","date":"2024-02-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/a-single-graph-convolution-is-all-you-need","slug":"a-single-graph-convolution-is-all-you-need","title":"A Single Graph Convolution Is All You Need: Efficient Grayscale Image Classification","date":"2024-02-01","arxiv_id":"2402.00564","n_code_links":1,"syntology":null},{"paper":null,"slug":"attention-based-dynamic-multilayer-graph","title":"Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction","date":"2024-02-01","arxiv_id":"2402.00299","n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-graph-for-multi-robot-social","title":"Attention Graph for Multi-Robot Social Navigation with Deep Reinforcement Learning","date":"2024-01-31","arxiv_id":"2401.17914","n_code_links":0,"syntology":null},{"paper":"/paper/heterophily-aware-fair-recommendation-using","slug":"heterophily-aware-fair-recommendation-using","title":"Heterophily-Aware Fair Recommendation using Graph Convolutional Networks","date":"2024-01-31","arxiv_id":"2402.03365","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-personalized-privacy-user-governed","title":"Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation","date":"2024-01-31","arxiv_id":"2401.17630","n_code_links":0,"syntology":null},{"paper":null,"slug":"online-algorithm-for-node-feature-forecasting","title":"Online Algorithm for Node Feature Forecasting in Temporal Graphs","date":"2024-01-30","arxiv_id":"2401.16800","n_code_links":0,"syntology":null},{"paper":null,"slug":"timeseries-suppliers-allocation-risk","title":"Time Series Supplier Allocation via Deep Black-Litterman Model","date":"2024-01-30","arxiv_id":"2401.17350","n_code_links":0,"syntology":null},{"paper":null,"slug":"spatio-temporal-attention-graph-neural","title":"Spatio-Temporal Attention Graph Neural Network for Remaining Useful Life Prediction","date":"2024-01-29","arxiv_id":"2401.15964","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-tuning-and-inference-for-large","slug":"efficient-tuning-and-inference-for-large","title":"Efficient Tuning and Inference for Large Language Models on Textual Graphs","date":"2024-01-28","arxiv_id":"2401.15569","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":1,"n_instrument":2,"unverified":1,"pointer_only":4,"phrase":"3 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; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["ZhuYun97/ENGINE"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/product-manifold-representations-for-learning","slug":"product-manifold-representations-for-learning","title":"Product Manifold Representations for Learning on Biological Pathways","date":"2024-01-27","arxiv_id":"2401.15478","n_code_links":2,"syntology":{"ran":6,"of":6,"n_ran_checked":2,"n_instrument":4,"unverified":0,"pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","official":{"repos":["mcneela/mixed-curvature-gcn","mcneela/mixed-curvature-pathways"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"edge-conditional-node-update-graph-neural","title":"Edge Conditional Node Update Graph Neural Network for Multi-variate Time Series Anomaly Detection","date":"2024-01-25","arxiv_id":"2401.13872","n_code_links":0,"syntology":null},{"paper":null,"slug":"manifold-gcn-diffusion-based-convolutional","title":"Manifold GCN: Diffusion-based Convolutional Neural Network for Manifold-valued Graphs","date":"2024-01-25","arxiv_id":"2401.14381","n_code_links":0,"syntology":null},{"paper":"/paper/truck-parking-usage-prediction-with","slug":"truck-parking-usage-prediction-with","title":"Truck Parking Usage Prediction with Decomposed Graph Neural Networks","date":"2024-01-23","arxiv_id":"2401.12920","n_code_links":1,"syntology":null},{"paper":null,"slug":"ada-gnn-atom-distance-angle-graph-neural","title":"ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction","date":"2024-01-22","arxiv_id":"2401.11768","n_code_links":0,"syntology":null},{"paper":null,"slug":"connecting-the-dots-leveraging-spatio","title":"Connecting the Dots: Leveraging Spatio-Temporal Graph Neural Networks for Accurate Bangla Sign Language Recognition","date":"2024-01-22","arxiv_id":"2401.12210","n_code_links":0,"syntology":null},{"paper":"/paper/full-body-motion-reconstruction-with-sparse","slug":"full-body-motion-reconstruction-with-sparse","title":"Full-Body Motion Reconstruction with Sparse Sensing from Graph Perspective","date":"2024-01-22","arxiv_id":"2401.11783","n_code_links":1,"syntology":null},{"paper":null,"slug":"knowledge-distillation-on-spatial-temporal","title":"Knowledge Distillation on Spatial-Temporal Graph Convolutional Network for Traffic Prediction","date":"2024-01-22","arxiv_id":"2401.11798","n_code_links":0,"syntology":null},{"paper":"/paper/learning-dynamics-from-multicellular-graphs","slug":"learning-dynamics-from-multicellular-graphs","title":"Learning Dynamics from Multicellular Graphs with Deep Neural Networks","date":"2024-01-22","arxiv_id":"2401.12196","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":["guolab-cellmechanics/gnn-collective-cell-dynamics"],"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":"patternportrait-draw-me-like-one-of-your","title":"PatternPortrait: Draw Me Like One of Your Scribbles","date":"2024-01-22","arxiv_id":"2401.13001","n_code_links":0,"syntology":null},{"paper":"/paper/tensor-view-topological-graph-neural-network","slug":"tensor-view-topological-graph-neural-network","title":"Tensor-view Topological Graph Neural Network","date":"2024-01-22","arxiv_id":"2401.12007","n_code_links":1,"syntology":null},{"paper":null,"slug":"graph-edits-for-counterfactual-explanations-a","title":"Graph Edits for Counterfactual Explanations: A comparative study","date":"2024-01-21","arxiv_id":"2401.11609","n_code_links":0,"syntology":null},{"paper":null,"slug":"mdgnn-multi-relational-dynamic-graph-neural","title":"MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction","date":"2024-01-19","arxiv_id":"2402.06633","n_code_links":0,"syntology":null},{"paper":"/paper/predicting-viral-rumors-and-vulnerable-users","slug":"predicting-viral-rumors-and-vulnerable-users","title":"Predicting Viral Rumors and Vulnerable Users for Infodemic Surveillance","date":"2024-01-18","arxiv_id":"2401.09724","n_code_links":1,"syntology":null},{"paper":"/paper/a-novel-hybrid-time-varying-graph-neural","slug":"a-novel-hybrid-time-varying-graph-neural","title":"A novel hybrid time-varying graph neural network for traffic flow forecasting","date":"2024-01-17","arxiv_id":"2401.10155","n_code_links":0,"syntology":null},{"paper":"/paper/gnn-lofi-a-novel-graph-neural-network-through","slug":"gnn-lofi-a-novel-graph-neural-network-through","title":"GNN-LoFI: a Novel Graph Neural Network through Localized Feature-based Histogram Intersection","date":"2024-01-17","arxiv_id":"2401.09193","n_code_links":1,"syntology":null},{"paper":null,"slug":"inverse-analysis-of-granular-flows-using","title":"Inverse analysis of granular flows using differentiable graph neural network simulator","date":"2024-01-17","arxiv_id":"2401.13695","n_code_links":0,"syntology":null},{"paper":null,"slug":"spoofing-detection-in-the-physical-layer-with","title":"Spoofing Detection in the Physical Layer with Graph Neural Networks","date":"2024-01-16","arxiv_id":"2401.08220","n_code_links":0,"syntology":null},{"paper":"/paper/on-the-power-of-graph-neural-networks-and","slug":"on-the-power-of-graph-neural-networks-and","title":"On the Power of Graph Neural Networks and Feature Augmentation Strategies to Classify Social Networks","date":"2024-01-11","arxiv_id":"2401.06048","n_code_links":1,"syntology":null},{"paper":"/paper/lpac-learnable-perception-action","slug":"lpac-learnable-perception-action","title":"LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control","date":"2024-01-10","arxiv_id":"2401.04855","n_code_links":1,"syntology":null},{"paper":null,"slug":"boosting-column-generation-with-graph-neural","title":"Boosting Column Generation with Graph Neural Networks for Joint Rider Trip Planning and Crew Shift Scheduling","date":"2024-01-08","arxiv_id":"2401.03692","n_code_links":0,"syntology":null},{"paper":null,"slug":"few-shot-causal-representation-learning-for","title":"Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs","date":"2024-01-07","arxiv_id":"2401.03597","n_code_links":0,"syntology":null},{"paper":null,"slug":"global-aware-enhanced-spatial-temporal-graph","title":"Global-Aware Enhanced Spatial-Temporal Graph Recurrent Networks: A New Framework For Traffic Flow Prediction","date":"2024-01-07","arxiv_id":"2401.04135","n_code_links":0,"syntology":null},{"paper":"/paper/global-prediction-of-covid-19-variant","slug":"global-prediction-of-covid-19-variant","title":"Global Prediction of COVID-19 Variant Emergence Using Dynamics-Informed Graph Neural Networks","date":"2024-01-07","arxiv_id":"2401.03390","n_code_links":1,"syntology":null},{"paper":null,"slug":"synhin-generating-synthetic-heterogeneous","title":"SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation","date":"2024-01-07","arxiv_id":"2401.04133","n_code_links":0,"syntology":null},{"paper":"/paper/teltrans-applying-multi-type-telecom-data-to","slug":"teltrans-applying-multi-type-telecom-data-to","title":"TelTrans: Applying Multi-Type Telecom Data to Transportation Evaluation and Prediction via Multifaceted Graph Modeling","date":"2024-01-06","arxiv_id":"2401.03138","n_code_links":0,"syntology":{"ran":2,"of":7,"n_ran_checked":2,"n_instrument":0,"unverified":5,"pointer_only":7,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","official":null}},{"paper":null,"slug":"a-topology-aware-graph-coarsening-framework","title":"A Topology-aware Graph Coarsening Framework for Continual Graph Learning","date":"2024-01-05","arxiv_id":"2401.03077","n_code_links":0,"syntology":null}],"record_sha256":"fb36f2fdf86131eb47579f88bb3448280a233c811bcf73663293adbf5a47ba82","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}