{"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-learning/papers/9","list_of":"/task/graph-learning","task":"Graph 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":9,"pages_in_order":16,"rows_per_page":100,"rows":[801,900],"of":1570,"counts":{"archive_papers_tagged":1570,"with_a_code_link":686,"where_syntology_ran_a_sample":193,"not_listed_spam_title":0,"listed":1570,"listed_where_code_ran":193,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":162,"every_run_a_failure_of_syntologys_instrument":31,"listed_with_a_run_with_no_instrument_failure":162,"listed_every_run_a_failure_of_syntologys_instrument":31,"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-learning","prev":"/task/graph-learning/papers/8","next":"/task/graph-learning/papers/10","papers":[{"url":null,"slug":"overcoming-class-imbalance-unified-gnn","title":"Overcoming Class Imbalance: Unified GNN Learning with Structural and Semantic Connectivity Representations","date":"2024-12-30","arxiv_id":"2412.20656","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-discovery-on-dependent-binary-data","title":"Causal Discovery on Dependent Binary Data","date":"2024-12-28","arxiv_id":"2412.20289","repositories_listed":0,"syntology":null},{"url":null,"slug":"ergnn-spectral-graph-neural-network-with","title":"ERGNN: Spectral Graph Neural Network With Explicitly-Optimized Rational Graph Filters","date":"2024-12-26","arxiv_id":"2412.19106","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-models-meet-graph-neural","title":"Large Language Models Meet Graph Neural Networks: A Perspective of Graph Mining","date":"2024-12-26","arxiv_id":"2412.19211","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-federated-graph-learning-via","title":"Enhancing Federated Graph Learning via Adaptive Fusion of Structural and Node Characteristics","date":"2024-12-25","arxiv_id":"2412.18845","repositories_listed":0,"syntology":null},{"url":null,"slug":"autosculpt-a-pattern-based-model-auto-pruning","title":"AutoSculpt: A Pattern-based Model Auto-pruning Framework Using Reinforcement Learning and Graph Learning","date":"2024-12-24","arxiv_id":"2412.18091","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-graph-mamba-a-comprehensive-survey","title":"Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning","date":"2024-12-24","arxiv_id":"2412.18322","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedgig-graph-inversion-from-gradient-in","title":"FedGIG: Graph Inversion from Gradient in Federated Learning","date":"2024-12-24","arxiv_id":"2412.18513","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-fuzzy-graph-attention-networks-for","title":"Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learning","date":"2024-12-23","arxiv_id":"2412.17271","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-learning-based-regional-heavy-rainfall","title":"Graph Learning-based Regional Heavy Rainfall Prediction Using Low-Cost Rain Gauges","date":"2024-12-22","arxiv_id":"2412.16842","repositories_listed":0,"syntology":null},{"url":null,"slug":"thegcn-temporal-heterophilic-graph","title":"THeGCN: Temporal Heterophilic Graph Convolutional Network","date":"2024-12-21","arxiv_id":"2412.16435","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedgat-a-privacy-preserving-federated","title":"FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks","date":"2024-12-20","arxiv_id":"2412.16144","repositories_listed":0,"syntology":null},{"url":null,"slug":"communication-efficient-personalized-federal","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","date":"2024-12-18","arxiv_id":"2412.13442","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-internet-of-things-security","title":"Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks","date":"2024-12-17","arxiv_id":"2412.13240","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-learning-in-the-era-of-llms-a-survey","title":"Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks","date":"2024-12-17","arxiv_id":"2412.12456","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-joint-graph-and-sampling-set","title":"Towards joint graph learning and sampling set selection from data","date":"2024-12-12","arxiv_id":"2412.09753","repositories_listed":0,"syntology":null},{"url":null,"slug":"ahsg-adversarial-attacks-on-high-level","title":"AHSG: Adversarial Attack on High-level Semantics in Graph Neural Networks","date":"2024-12-10","arxiv_id":"2412.07468","repositories_listed":0,"syntology":null},{"url":null,"slug":"my-words-imply-your-opinion-reader-agent","title":"My Words Imply Your Opinion: Reader Agent-Based Propagation Enhancement for Personalized Implicit Emotion Analysis","date":"2024-12-10","arxiv_id":"2412.07367","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-graph-foundation-models-a-study-on","title":"Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings","date":"2024-12-10","arxiv_id":"2412.07407","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-graph-learning-from-spatial","title":"Adaptive Graph Learning from Spatial Information for Surgical Workflow Anticipation","date":"2024-12-09","arxiv_id":"2412.06454","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-the-necessity-of-graph-learning","title":"Revisiting the Necessity of Graph Learning and Common Graph Benchmarks","date":"2024-12-09","arxiv_id":"2412.06173","repositories_listed":0,"syntology":null},{"url":null,"slug":"node-classification-with-integrated-reject","title":"Node Classification With Integrated Reject Option","date":"2024-12-04","arxiv_id":"2412.03190","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-learning-for-planning-the-story-thus","title":"Graph Learning for Planning: The Story Thus Far and Open Challenges","date":"2024-12-03","arxiv_id":"2412.02136","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybridmqa-exploring-geometry-texture","title":"HybridMQA: Exploring Geometry-Texture Interactions for Colored Mesh Quality Assessment","date":"2024-12-02","arxiv_id":"2412.01986","repositories_listed":0,"syntology":null},{"url":null,"slug":"rehub-linear-complexity-graph-transformers","title":"ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment","date":"2024-12-02","arxiv_id":"2412.01519","repositories_listed":0,"syntology":null},{"url":null,"slug":"signal-processing-over-time-varying-graphs-a","title":"Signal Processing over Time-Varying Graphs: A Systematic Review","date":"2024-11-30","arxiv_id":"2412.00462","repositories_listed":0,"syntology":null},{"url":null,"slug":"attribute-enhanced-similarity-ranking-for","title":"Attribute-Enhanced Similarity Ranking for Sparse Link Prediction","date":"2024-11-29","arxiv_id":"2412.00261","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedrgl-robust-federated-graph-learning-for","title":"FedRGL: Robust Federated Graph Learning for Label Noise","date":"2024-11-28","arxiv_id":"2411.18905","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-data-centric-machine-learning-on","title":"Towards Data-centric Machine Learning on Directed Graphs: a Survey","date":"2024-11-28","arxiv_id":"2412.01849","repositories_listed":0,"syntology":null},{"url":"/paper/heterophilic-graph-neural-networks","slug":"heterophilic-graph-neural-networks","title":"Heterophilic Graph Neural Networks Optimization with Causal Message-passing","date":"2024-11-21","arxiv_id":"2411.13821","repositories_listed":0,"syntology":null},{"url":null,"slug":"aglp-a-graph-learning-perspective-for-semi","title":"AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation","date":"2024-11-20","arxiv_id":"2411.13152","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-and-robust-continual-graph-learning","title":"Efficient and Robust Continual Graph Learning for Graph Classification in Biology","date":"2024-11-18","arxiv_id":"2411.11668","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-retention-networks-for-dynamic-graphs","title":"Graph Retention Networks for Dynamic Graphs","date":"2024-11-18","arxiv_id":"2411.11259","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-personalized-federated-node","title":"Towards Federated Graph Learning in One-shot Communication","date":"2024-11-18","arxiv_id":"2411.11304","repositories_listed":0,"syntology":null},{"url":null,"slug":"impact-gnn-imposing-invariance-with-message","title":"IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs","date":"2024-11-17","arxiv_id":"2411.10957","repositories_listed":0,"syntology":null},{"url":null,"slug":"continuous-gnn-based-anomaly-detection-on","title":"Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning","date":"2024-11-13","arxiv_id":"2411.09072","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-graph-learning-with-graphless","title":"Federated Graph Learning with Graphless Clients","date":"2024-11-13","arxiv_id":"2411.08374","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-and-robust-contextual-node","title":"Fast and Robust Contextual Node Representation Learning over Dynamic Graphs","date":"2024-11-11","arxiv_id":"2411.07123","repositories_listed":0,"syntology":null},{"url":null,"slug":"shedding-light-on-problems-with-hyperbolic","title":"Shedding Light on Problems with Hyperbolic Graph Learning","date":"2024-11-11","arxiv_id":"2411.06688","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-kolmogorov-arnold-network","title":"A Survey on Kolmogorov-Arnold Network","date":"2024-11-09","arxiv_id":"2411.06078","repositories_listed":0,"syntology":null},{"url":null,"slug":"against-multifaceted-graph-heterogeneity-via","title":"Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning","date":"2024-11-04","arxiv_id":"2411.02003","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-graph-neural-network-states-contain-graph","title":"Do graph neural network states contain graph properties?","date":"2024-11-04","arxiv_id":"2411.02168","repositories_listed":0,"syntology":null},{"url":null,"slug":"g-sparc-spectral-architectures-tackling-the","title":"G-SPARC: SPectral ARchitectures tackling the Cold-start problem in Graph learning","date":"2024-11-03","arxiv_id":"2411.01532","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-graph-learning-via-time-vertex","title":"Online Graph Learning via Time-Vertex Adaptive Filters: From Theory to Cardiac Fibrillation","date":"2024-11-03","arxiv_id":"2411.01567","repositories_listed":0,"syntology":null},{"url":null,"slug":"network-games-induced-prior-for-graph","title":"Network Games Induced Prior for Graph Topology Learning","date":"2024-10-31","arxiv_id":"2410.24095","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-graph-learning-approach-for","title":"End-to-end Graph Learning Approach for Cognitive Diagnosis of Student Tutorial","date":"2024-10-30","arxiv_id":"2411.00845","repositories_listed":0,"syntology":null},{"url":null,"slug":"reliable-and-compact-graph-fine-tuning-via","title":"Reliable and Compact Graph Fine-tuning via GraphSparse Prompting","date":"2024-10-29","arxiv_id":"2410.21749","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarking-graph-learning-for-drug-drug","title":"Benchmarking Graph Learning for Drug-Drug Interaction Prediction","date":"2024-10-24","arxiv_id":"2410.18583","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-self-supervision-rejuvenate-similarity","title":"Can Self Supervision Rejuvenate Similarity-Based Link Prediction?","date":"2024-10-24","arxiv_id":"2410.19183","repositories_listed":0,"syntology":null},{"url":null,"slug":"perturbation-based-graph-active-learning-for","title":"Perturbation-based Graph Active Learning for Weakly-Supervised Belief Representation Learning","date":"2024-10-24","arxiv_id":"2410.19176","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-graph-covariate-shift-via-score","title":"Mitigating Graph Covariate Shift via Score-based Out-of-distribution Augmentation","date":"2024-10-23","arxiv_id":"2410.17506","repositories_listed":0,"syntology":null},{"url":null,"slug":"tagexplainer-narrating-graph-explanations-for","title":"TAGExplainer: Narrating Graph Explanations for Text-Attributed Graph Learning Models","date":"2024-10-20","arxiv_id":"2410.15268","repositories_listed":0,"syntology":null},{"url":null,"slug":"langgfm-a-large-language-model-alone-can-be-a","title":"LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model","date":"2024-10-19","arxiv_id":"2410.14961","repositories_listed":0,"syntology":null},{"url":null,"slug":"conformal-prediction-for-federated-graph","title":"Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information","date":"2024-10-17","arxiv_id":"2410.14010","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-masked-autoencoder-for-spatio-temporal","title":"Graph Masked Autoencoder for Spatio-Temporal Graph Learning","date":"2024-10-14","arxiv_id":"2410.10915","repositories_listed":0,"syntology":null},{"url":null,"slug":"nt-llm-a-novel-node-tokenizer-for-integrating","title":"NT-LLM: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models","date":"2024-10-14","arxiv_id":"2410.10743","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-multi-modal-root-cause-analysis","title":"Online Multi-modal Root Cause Analysis","date":"2024-10-13","arxiv_id":"2410.10021","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-stable-globally-expressive-graph","title":"Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors","date":"2024-10-13","arxiv_id":"2410.09737","repositories_listed":0,"syntology":null},{"url":null,"slug":"deeper-insights-into-deep-graph-convolutional","title":"Deeper Insights into Deep Graph Convolutional Networks: Stability and Generalization","date":"2024-10-11","arxiv_id":"2410.08473","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-graph-learning-for-cross-domain","title":"Federated Graph Learning for Cross-Domain Recommendation","date":"2024-10-10","arxiv_id":"2410.08249","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalizable-indoor-human-activity","title":"Generalizable Indoor Human Activity Recognition Method Based on Micro-Doppler Corner Point Cloud and Dynamic Graph Learning","date":"2024-10-10","arxiv_id":"2410.07542","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalization-from-starvation-hints-of","title":"Generalization from Starvation: Hints of Universality in LLM Knowledge Graph Learning","date":"2024-10-10","arxiv_id":"2410.08255","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-first-order-algorithm-for-graph-learning","title":"Network Topology Inference from Smooth Signals Under Partial Observability","date":"2024-10-08","arxiv_id":"2410.05707","repositories_listed":0,"syntology":null},{"url":null,"slug":"defense-as-a-service-black-box-shielding","title":"Defense-as-a-Service: Black-box Shielding against Backdoored Graph Models","date":"2024-10-07","arxiv_id":"2410.04916","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-stage-graph-learning-for-fmri-analysis","title":"Multi-Stage Graph Learning for fMRI Analysis to Diagnose Neuro-Developmental Disorders","date":"2024-10-07","arxiv_id":"2410.05342","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-graph-self-supervised-learning-with","title":"Enhancing Graph Self-Supervised Learning with Graph Interplay","date":"2024-10-05","arxiv_id":"2410.04061","repositories_listed":0,"syntology":null},{"url":null,"slug":"similarity-enhanced-homophily-for-multi-view","title":"SiMilarity-Enhanced Homophily for Multi-View Heterophilous Graph Clustering","date":"2024-10-04","arxiv_id":"2410.03596","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-models-are-graph-learners","title":"How to Make LLMs Strong Node Classifiers?","date":"2024-10-03","arxiv_id":"2410.02296","repositories_listed":0,"syntology":null},{"url":null,"slug":"phympgn-physics-encoded-message-passing-graph","title":"PhyMPGN: Physics-encoded Message Passing Graph Network for spatiotemporal PDE systems","date":"2024-10-02","arxiv_id":"2410.01337","repositories_listed":0,"syntology":null},{"url":null,"slug":"gundam-aligning-large-language-models-with","title":"GUNDAM: Aligning Large Language Models with Graph Understanding","date":"2024-09-30","arxiv_id":"2409.20053","repositories_listed":0,"syntology":null},{"url":null,"slug":"convergence-aware-clustered-federated-graph","title":"Convergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting","date":"2024-09-29","arxiv_id":"2409.19545","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-node-per-user-node-level-federated","title":"One Node Per User: Node-Level Federated Learning for Graph Neural Networks","date":"2024-09-29","arxiv_id":"2409.19513","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-impact-of-feature-heterophily-on-link","title":"On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks","date":"2024-09-26","arxiv_id":"2409.17475","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-graph-learning-with-adaptive","title":"Federated Graph Learning with Adaptive Importance-based Sampling","date":"2024-09-23","arxiv_id":"2409.14655","repositories_listed":0,"syntology":null},{"url":null,"slug":"hydrovision-lidar-guided-hydrometric","title":"HydroVision: LiDAR-Guided Hydrometric Prediction with Vision Transformers and Hybrid Graph Learning","date":"2024-09-23","arxiv_id":"2409.15213","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-proximal-admm-for-graph-learning-from","title":"Online Proximal ADMM for Graph Learning from Streaming Smooth Signals","date":"2024-09-19","arxiv_id":"2409.12916","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedne-surrogate-assisted-federated-neighbor","title":"FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction","date":"2024-09-17","arxiv_id":"2409.11509","repositories_listed":0,"syntology":null},{"url":null,"slug":"flexible-diffusion-scopes-with-parameterized","title":"Flexible Diffusion Scopes with Parameterized Laplacian for Heterophilic Graph Learning","date":"2024-09-15","arxiv_id":"2409.09888","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-network-inference-from-graph","title":"Online Network Inference from Graph-Stationary Signals with Hidden Nodes","date":"2024-09-13","arxiv_id":"2409.08760","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-learning-of-balanced-signed-graphs","title":"Efficient Learning of Balanced Signed Graphs via Iterative Linear Programming","date":"2024-09-12","arxiv_id":"2409.07794","repositories_listed":0,"syntology":null},{"url":null,"slug":"virtual-node-generation-for-node","title":"Virtual Node Generation for Node Classification in Sparsely-Labeled Graphs","date":"2024-09-12","arxiv_id":"2409.07712","repositories_listed":0,"syntology":null},{"url":null,"slug":"mcdgln-masked-connection-based-dynamic-graph","title":"MCDGLN: Masked Connection-based Dynamic Graph Learning Network for Autism Spectrum Disorder","date":"2024-09-10","arxiv_id":"2409.06163","repositories_listed":0,"syntology":null},{"url":null,"slug":"latex-gcl-large-language-models-llms-based","title":"LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning","date":"2024-09-02","arxiv_id":"2409.01145","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-adversarial-perturbators-generate-rich","title":"Dual Adversarial Perturbators Generate rich Views for Recommendation","date":"2024-08-26","arxiv_id":"2409.06719","repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangled-generative-graph-representation","title":"Disentangled Generative Graph Representation Learning","date":"2024-08-24","arxiv_id":"2408.13471","repositories_listed":0,"syntology":null},{"url":null,"slug":"llm-enhanced-scene-graph-learning-for","title":"LLM-enhanced Scene Graph Learning for Household Rearrangement","date":"2024-08-22","arxiv_id":"2408.12093","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-federated-graph-learning-with","title":"Optimizing Federated Graph Learning with Inherent Structural Knowledge and Dual-Densely Connected GNNs","date":"2024-08-21","arxiv_id":"2408.11662","repositories_listed":0,"syntology":null},{"url":null,"slug":"slicing-input-features-to-accelerate-deep","title":"Slicing Input Features to Accelerate Deep Learning: A Case Study with Graph Neural Networks","date":"2024-08-21","arxiv_id":"2408.11500","repositories_listed":0,"syntology":null},{"url":null,"slug":"asymmetric-graph-error-control-with-low","title":"Asymmetric Graph Error Control with Low Complexity in Causal Bandits","date":"2024-08-20","arxiv_id":"2408.11240","repositories_listed":0,"syntology":null},{"url":null,"slug":"grassnet-state-space-model-meets-graph-neural","title":"GrassNet: State Space Model Meets Graph Neural Network","date":"2024-08-16","arxiv_id":"2408.08583","repositories_listed":0,"syntology":null},{"url":null,"slug":"battery-graphnets-relational-learning-for","title":"Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation","date":"2024-08-14","arxiv_id":"2408.07624","repositories_listed":0,"syntology":null},{"url":"/paper/dyg-mamba-continuous-state-space-modeling-on","slug":"dyg-mamba-continuous-state-space-modeling-on","title":"DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs","date":"2024-08-13","arxiv_id":"2408.06966","repositories_listed":0,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/dyg-mamba-continuous-state-space-modeling-on#ran","syntology_url":"https://syntology.ai/paper/2408.06966","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.06966"}},"official":null}},{"url":null,"slug":"a-structural-feature-based-approach-for","title":"A Structural Feature-Based Approach for Comprehensive Graph Classification","date":"2024-08-10","arxiv_id":"2408.05474","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-multi-step-scientific-processes-with","title":"Modeling Multi-Step Scientific Processes with Graph Transformer Networks","date":"2024-08-10","arxiv_id":"2408.05425","repositories_listed":0,"syntology":null},{"url":null,"slug":"path-llm-a-shortest-path-based-llm-learning","title":"Path-LLM: A Shortest-Path-based LLM Learning for Unified Graph Representation","date":"2024-08-10","arxiv_id":"2408.05456","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-hypergraph-learning-with-hyperedge","title":"Federated Hypergraph Learning: Hyperedge Completion with Local Differential Privacy","date":"2024-08-09","arxiv_id":"2408.05160","repositories_listed":0,"syntology":null},{"url":null,"slug":"node-level-graph-autoencoder-unified","title":"Node Level Graph Autoencoder: Unified Pretraining for Textual Graph Learning","date":"2024-08-09","arxiv_id":"2408.07091","repositories_listed":0,"syntology":null},{"url":null,"slug":"dive-subgraph-disagreement-for-graph-out-of","title":"DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization","date":"2024-08-08","arxiv_id":"2408.04400","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-contrastive-graph-clustering","title":"Self-Supervised Contrastive Graph Clustering Network via Structural Information Fusion","date":"2024-08-08","arxiv_id":"2408.04339","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-probing-for-graph-representation","title":"Knowledge Probing for Graph Representation Learning","date":"2024-08-07","arxiv_id":"2408.03877","repositories_listed":0,"syntology":null}],"record_sha256":"ea6cd2cd5b33d639a7e24366edf100140b4972ce3b10f8e906b1cf0ce30bd65d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}