{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/graph-representation-learning/papers/6","list_of":"/task/graph-representation-learning","task":"Graph Representation Learning","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":6,"pages_in_order":10,"rows_per_page":100,"rows":[501,600],"of":982,"counts":{"archive_papers_tagged":982,"with_a_code_link":479,"where_syntology_ran_a_sample":129,"not_listed_spam_title":0,"listed":982,"listed_where_code_ran":129,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":115,"every_run_a_failure_of_syntologys_instrument":14,"listed_with_a_run_with_no_instrument_failure":115,"listed_every_run_a_failure_of_syntologys_instrument":14,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/graph-representation-learning","prev":"/task/graph-representation-learning/papers/5","next":"/task/graph-representation-learning/papers/7","papers":[{"url":null,"slug":"omnisage-large-scale-multi-entity","title":"OmniSage: Large Scale, Multi-Entity Heterogeneous Graph Representation Learning","date":"2025-04-22","arxiv_id":"2504.17811","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-spatio-temporal-graph-learning-for","title":"Multimodal Spatio-temporal Graph Learning for Alignment-free RGBT Video Object Detection","date":"2025-04-16","arxiv_id":"2504.11779","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-auto-distillation-and-generative","title":"Leveraging Auto-Distillation and Generative Self-Supervised Learning in Residual Graph Transformers for Enhanced Recommender Systems","date":"2025-04-08","arxiv_id":"2504.10500","repositories_listed":0,"syntology":null},{"url":null,"slug":"inductive-graph-representation-learning-with-1","title":"Inductive Graph Representation Learning with Quantum Graph Neural Networks","date":"2025-03-31","arxiv_id":"2503.24111","repositories_listed":0,"syntology":null},{"url":null,"slug":"node-embeddings-via-neighbor-embeddings","title":"Node Embeddings via Neighbor Embeddings","date":"2025-03-31","arxiv_id":"2503.23822","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-based-re-ranking-emerging-techniques","title":"Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities","date":"2025-03-19","arxiv_id":"2503.14802","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-node-pruning-for-accurate-graph","title":"Multi-View Node Pruning for Accurate Graph Representation","date":"2025-03-14","arxiv_id":"2503.11737","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-tuning-vision-language-models-with-graph","title":"Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis","date":"2025-03-12","arxiv_id":"2503.09808","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffusion-model-agnostic-social-influence","title":"Diffusion Model Agnostic Social Influence Maximization in Hyperbolic Space","date":"2025-02-19","arxiv_id":"2502.13571","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-gnn-based-spectral-filtering-mechanism-for","title":"Graph Neural Network-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin","date":"2025-02-17","arxiv_id":"2502.11505","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-active-learning-based-experimental","title":"Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics","date":"2025-02-03","arxiv_id":"2502.01012","repositories_listed":0,"syntology":null},{"url":"/paper/spectro-riemannian-graph-neural-networks","slug":"spectro-riemannian-graph-neural-networks","title":"Spectro-Riemannian Graph Neural Networks","date":"2025-02-01","arxiv_id":"2502.00401","repositories_listed":0,"syntology":{"n":6,"n_ran":3,"n_constructed":2,"n_ran_checked":2,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":6,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/spectro-riemannian-graph-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2502.00401","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.00401"}},"official":null}},{"url":null,"slug":"contrastive-learning-meets-pseudo-label","title":"Contrastive Learning Meets Pseudo-label-assisted Mixup Augmentation: A Comprehensive Graph Representation Framework from Local to Global","date":"2025-01-30","arxiv_id":"2501.18357","repositories_listed":0,"syntology":null},{"url":"/paper/mamba-based-graph-convolutional-networks","slug":"mamba-based-graph-convolutional-networks","title":"Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space","date":"2025-01-26","arxiv_id":"2501.15461","repositories_listed":0,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 2 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) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/mamba-based-graph-convolutional-networks#ran","syntology_url":"https://syntology.ai/paper/2501.15461","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.15461"}},"official":null}},{"url":"/paper/deep-modularity-networks-with-diversity","slug":"deep-modularity-networks-with-diversity","title":"Deep Modularity Networks with Diversity--Preserving Regularization","date":"2025-01-23","arxiv_id":"2501.13451","repositories_listed":0,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 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; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/deep-modularity-networks-with-diversity#ran","syntology_url":"https://syntology.ai/paper/2501.13451","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.13451"}},"official":null}},{"url":null,"slug":"graph-representation-learning-with-diffusion","title":"Graph Representation Learning with Diffusion Generative Models","date":"2025-01-22","arxiv_id":"2501.13133","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-blockchain-analysis-tackling","title":"Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks","date":"2025-01-21","arxiv_id":"2501.12491","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-supply-chain-networks-with-the","title":"Optimizing Supply Chain Networks with the Power of Graph Neural Networks","date":"2025-01-07","arxiv_id":"2501.06221","repositories_listed":0,"syntology":null},{"url":null,"slug":"kan-kan-buff-signed-graph-neural-networks","title":"KAN KAN Buff Signed Graph Neural Networks?","date":"2025-01-01","arxiv_id":"2501.00709","repositories_listed":0,"syntology":null},{"url":null,"slug":"scam-detection-for-ethereum-smart-contracts","title":"Scam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchain","date":"2024-12-16","arxiv_id":"2412.12370","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-study-on-dynamic-graph","title":"A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers","date":"2024-12-15","arxiv_id":"2412.11293","repositories_listed":0,"syntology":null},{"url":null,"slug":"bootstrapping-heterogeneous-graph","title":"Bootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approach","date":"2024-12-11","arxiv_id":"2412.08038","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-graph-representation-learning","title":"Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning","date":"2024-12-10","arxiv_id":"2412.07809","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-self-guided-multimodal-approach-to","title":"A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases","date":"2024-12-09","arxiv_id":"2412.06212","repositories_listed":0,"syntology":null},{"url":null,"slug":"expressivity-of-representation-learning-on","title":"Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review","date":"2024-12-05","arxiv_id":"2412.03783","repositories_listed":0,"syntology":null},{"url":null,"slug":"gqwformer-a-quantum-based-transformer-for","title":"GQWformer: A Quantum-based Transformer for Graph Representation Learning","date":"2024-12-03","arxiv_id":"2412.02285","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-fair-graph-neural-networks-via-dual","title":"Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation","date":"2024-11-30","arxiv_id":"2412.00382","repositories_listed":0,"syntology":null},{"url":null,"slug":"perturbation-ontology-based-graph-attention","title":"Perturbation Ontology based Graph Attention Networks","date":"2024-11-27","arxiv_id":"2411.18520","repositories_listed":0,"syntology":null},{"url":null,"slug":"instance-aware-graph-prompt-learning","title":"Instance-Aware Graph Prompt Learning","date":"2024-11-26","arxiv_id":"2411.17676","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-graph-deep-representation","title":"A survey on Graph Deep Representation Learning for Facial Expression Recognition","date":"2024-11-13","arxiv_id":"2411.08472","repositories_listed":0,"syntology":null},{"url":null,"slug":"heterosample-meta-path-guided-sampling-for","title":"HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning","date":"2024-11-11","arxiv_id":"2411.07022","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":"learning-from-graph-structured-data","title":"Learning From Graph-Structured Data: Addressing Design Issues and Exploring Practical Applications in Graph Representation Learning","date":"2024-11-09","arxiv_id":"2411.07269","repositories_listed":0,"syntology":null},{"url":null,"slug":"post-hoc-robustness-enhancement-in-graph","title":"Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields","date":"2024-11-08","arxiv_id":"2411.05399","repositories_listed":0,"syntology":null},{"url":null,"slug":"decrl-a-deep-evolutionary-clustering-jointed","title":"DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach","date":"2024-10-30","arxiv_id":"2410.22631","repositories_listed":0,"syntology":null},{"url":null,"slug":"synergizing-llm-agents-and-knowledge-graph","title":"Synergizing LLM Agents and Knowledge Graph for Socioeconomic Prediction in LBSN","date":"2024-10-29","arxiv_id":"2411.00028","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-decomposition-of-graph-neural-networks","title":"Sparse Decomposition of Graph Neural Networks","date":"2024-10-25","arxiv_id":"2410.19723","repositories_listed":0,"syntology":null},{"url":null,"slug":"bridging-large-language-models-and-graph","title":"Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning","date":"2024-10-15","arxiv_id":"2410.12096","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-fair-graph-representation-learning-in","title":"Towards Fair Graph Representation Learning in Social Networks","date":"2024-10-15","arxiv_id":"2410.11493","repositories_listed":0,"syntology":null},{"url":null,"slug":"information-propagation-dynamics-in-deep","title":"Information propagation dynamics in Deep Graph Networks","date":"2024-10-14","arxiv_id":"2410.10464","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-benchmark-on-directed-graph-representation","title":"A Benchmark on Directed Graph Representation Learning in Hardware Designs","date":"2024-10-09","arxiv_id":"2410.06460","repositories_listed":0,"syntology":null},{"url":null,"slug":"haste-makes-waste-a-simple-approach-for","title":"Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks","date":"2024-10-07","arxiv_id":"2410.05416","repositories_listed":0,"syntology":null},{"url":null,"slug":"classcontrast-bridging-the-spatial-and","title":"ClassContrast: Bridging the Spatial and Contextual Gaps for Node Representations","date":"2024-10-03","arxiv_id":"2410.02158","repositories_listed":0,"syntology":null},{"url":null,"slug":"toper-topological-embeddings-in-graph","title":"TopER: Topological Embeddings in Graph Representation Learning","date":"2024-10-02","arxiv_id":"2410.01778","repositories_listed":0,"syntology":null},{"url":null,"slug":"verbalized-graph-representation-learning-a","title":"Verbalized Graph Representation Learning: A Fully Interpretable Graph Model Based on Large Language Models Throughout the Entire Process","date":"2024-10-02","arxiv_id":"2410.01457","repositories_listed":0,"syntology":null},{"url":null,"slug":"heterogeneous-hyper-graph-neural-networks-for","title":"Heterogeneous Hyper-Graph Neural Networks for Context-aware Human Activity Recognition","date":"2024-09-26","arxiv_id":"2409.17483","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-graph-pooling-based-on-minimum","title":"MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length","date":"2024-09-16","arxiv_id":"2409.10263","repositories_listed":0,"syntology":null},{"url":null,"slug":"gre-2-mdcl-graph-representation-embedding","title":"GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning","date":"2024-09-12","arxiv_id":"2409.07725","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-object-event-graph-representation","title":"Multi-object event graph representation learning for Video Question Answering","date":"2024-09-12","arxiv_id":"2409.07747","repositories_listed":0,"syntology":null},{"url":null,"slug":"ethereum-fraud-detection-via-joint","title":"Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning","date":"2024-09-09","arxiv_id":"2409.07494","repositories_listed":0,"syntology":null},{"url":null,"slug":"graffin-stand-for-tails-in-imbalanced-node","title":"Graffin: Stand for Tails in Imbalanced Node Classification","date":"2024-09-09","arxiv_id":"2409.05339","repositories_listed":0,"syntology":null},{"url":null,"slug":"mtlso-a-multi-task-learning-approach-for","title":"MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization","date":"2024-09-09","arxiv_id":"2409.06077","repositories_listed":0,"syntology":null},{"url":null,"slug":"debiasing-graph-representation-learning-based","title":"Debiasing Graph Representation Learning based on Information Bottleneck","date":"2024-09-02","arxiv_id":"2409.01367","repositories_listed":0,"syntology":null},{"url":null,"slug":"pslf-a-pid-controller-incorporated-second","title":"PSLF: A PID Controller-incorporated Second-order Latent Factor Analysis Model for Recommender System","date":"2024-08-31","arxiv_id":"2409.00448","repositories_listed":0,"syntology":null},{"url":null,"slug":"sihgnn-leveraging-properties-of-semantic","title":"SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration","date":"2024-08-27","arxiv_id":"2408.15089","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-spacetimes-for-dag-representation","title":"Neural Spacetimes for DAG Representation Learning","date":"2024-08-25","arxiv_id":"2408.13885","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":"dynamic-graph-representation-learning-for-1","title":"Dynamic Graph Representation Learning for Passenger Behavior Prediction","date":"2024-08-17","arxiv_id":"2408.09092","repositories_listed":0,"syntology":null},{"url":null,"slug":"cegrl-tkgr-a-causal-enhanced-graph","title":"CEGRL-TKGR: A Causal Enhanced Graph Representation Learning Framework for Temporal Knowledge Graph Reasoning","date":"2024-08-15","arxiv_id":"2408.07911","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":"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":"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},{"url":null,"slug":"2408-02704","title":"Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning","date":"2024-08-05","arxiv_id":"2408.02704","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-graph-representation-learning","title":"Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck","date":"2024-08-01","arxiv_id":"2408.00295","repositories_listed":0,"syntology":null},{"url":"/paper/harvesting-textual-and-structured-data-from","slug":"harvesting-textual-and-structured-data-from","title":"Harvesting Textual and Structured Data from the HAL Publication Repository","date":"2024-07-30","arxiv_id":"2407.20595","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-multi-facet-paths-for","title":"Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learning","date":"2024-07-30","arxiv_id":"2407.20648","repositories_listed":0,"syntology":null},{"url":null,"slug":"unveiling-the-potential-of-spiking-dynamics","title":"Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies","date":"2024-07-30","arxiv_id":"2407.20508","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-communication-enhanced-by-knowledge","title":"Semantic Communication Enhanced by Knowledge Graph Representation Learning","date":"2024-07-27","arxiv_id":"2407.19338","repositories_listed":0,"syntology":null},{"url":null,"slug":"dtformer-a-transformer-based-method-for","title":"DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning","date":"2024-07-26","arxiv_id":"2407.18523","repositories_listed":0,"syntology":null},{"url":null,"slug":"your-graph-recommender-is-provably-a-single","title":"Your Graph Recommender is Provably a Single-view Graph Contrastive Learning","date":"2024-07-25","arxiv_id":"2407.17723","repositories_listed":0,"syntology":null},{"url":null,"slug":"hhgt-hierarchical-heterogeneous-graph","title":"HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning","date":"2024-07-18","arxiv_id":"2407.13158","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-heterophilic-graph-learning-handbook","title":"The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges","date":"2024-07-12","arxiv_id":"2407.09618","repositories_listed":0,"syntology":null},{"url":null,"slug":"edge-graph-intelligence-reciprocally","title":"Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence","date":"2024-07-07","arxiv_id":"2407.15320","repositories_listed":0,"syntology":null},{"url":null,"slug":"differential-encoding-for-improved","title":"Differential Encoding for Improved Representation Learning over Graphs","date":"2024-07-03","arxiv_id":"2407.02758","repositories_listed":0,"syntology":null},{"url":null,"slug":"heterogeneous-graph-contrastive-learning-with-1","title":"Heterogeneous Graph Contrastive Learning with Spectral Augmentation","date":"2024-06-30","arxiv_id":"2407.00708","repositories_listed":0,"syntology":null},{"url":null,"slug":"inference-of-sequential-patterns-for-neural","title":"Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs","date":"2024-06-24","arxiv_id":"2406.16552","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-representation-learning-strategies-for","title":"Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease","date":"2024-06-20","arxiv_id":"2406.14442","repositories_listed":0,"syntology":null},{"url":null,"slug":"harvesting-efficient-on-demand-order-pooling","title":"Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments","date":"2024-06-20","arxiv_id":"2406.14635","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-edge-wise-representation-learning","title":"Effective Edge-wise Representation Learning in Edge-Attributed Bipartite Graphs","date":"2024-06-19","arxiv_id":"2406.13369","repositories_listed":0,"syntology":null},{"url":null,"slug":"robgc-towards-robust-graph-condensation","title":"RobGC: Towards Robust Graph Condensation","date":"2024-06-19","arxiv_id":"2406.13200","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-scalable-and-effective-alternative-to-graph","title":"A Scalable and Effective Alternative to Graph Transformers","date":"2024-06-17","arxiv_id":"2406.12059","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-unified-graph-selective-prompt-learning-for","title":"A Unified Graph Selective Prompt Learning for Graph Neural Networks","date":"2024-06-15","arxiv_id":"2406.10498","repositories_listed":0,"syntology":null},{"url":null,"slug":"introducing-diminutive-causal-structure-into","title":"Introducing Diminutive Causal Structure into Graph Representation Learning","date":"2024-06-13","arxiv_id":"2406.08709","repositories_listed":0,"syntology":null},{"url":null,"slug":"augmentation-based-unsupervised-cross-domain","title":"Augmentation-based Unsupervised Cross-Domain Functional MRI Adaptation for Major Depressive Disorder Identification","date":"2024-05-31","arxiv_id":"2406.00085","repositories_listed":0,"syntology":null},{"url":"/paper/injecting-hamiltonian-architectural-bias-into","slug":"injecting-hamiltonian-architectural-bias-into","title":"Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks","date":"2024-05-27","arxiv_id":"2405.17163","repositories_listed":0,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_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","sample_list":"/paper/injecting-hamiltonian-architectural-bias-into#ran","syntology_url":"https://syntology.ai/paper/2405.17163","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.17163"}},"official":null}},{"url":null,"slug":"graphlets-correct-for-the-topological","title":"Graphlets correct for the topological information missed by random walks","date":"2024-05-23","arxiv_id":"2405.14194","repositories_listed":0,"syntology":null},{"url":null,"slug":"hc-gae-the-hierarchical-cluster-based-graph","title":"HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning","date":"2024-05-23","arxiv_id":"2405.14742","repositories_listed":0,"syntology":null},{"url":null,"slug":"relating-up-advancing-graph-neural-networks","title":"Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships","date":"2024-05-07","arxiv_id":"2405.03950","repositories_listed":0,"syntology":null},{"url":null,"slug":"anchorgt-efficient-and-flexible-attention","title":"AnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformers","date":"2024-05-06","arxiv_id":"2405.03481","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameter-efficient-tuning-large-language","title":"Parameter-Efficient Tuning Large Language Models for Graph Representation Learning","date":"2024-04-28","arxiv_id":"2404.18271","repositories_listed":0,"syntology":null},{"url":null,"slug":"interaction-event-forecasting-in-multi","title":"Deep Representation Learning for Forecasting Recursive and Multi-Relational Events in Temporal Networks","date":"2024-04-27","arxiv_id":"2404.17943","repositories_listed":0,"syntology":null},{"url":null,"slug":"delayed-bottlenecking-alleviating-forgetting","title":"Delayed Bottlenecking: Alleviating Forgetting in Pre-trained Graph Neural Networks","date":"2024-04-23","arxiv_id":"2404.14941","repositories_listed":0,"syntology":null},{"url":null,"slug":"graphmatcher-a-graph-representation-learning","title":"GraphMatcher: A Graph Representation Learning Approach for Ontology Matching","date":"2024-04-20","arxiv_id":"2404.14450","repositories_listed":0,"syntology":null},{"url":null,"slug":"core-data-augmentation-for-link-prediction","title":"CORE: Data Augmentation for Link Prediction via Information Bottleneck","date":"2024-04-17","arxiv_id":"2404.11032","repositories_listed":0,"syntology":null},{"url":null,"slug":"higraphdti-hierarchical-graph-representation","title":"HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction","date":"2024-04-16","arxiv_id":"2404.10561","repositories_listed":0,"syntology":null},{"url":null,"slug":"neighbour-level-message-interaction-encoding","title":"Neighbour-level Message Interaction Encoding for Improved Representation Learning on Graphs","date":"2024-04-15","arxiv_id":"2404.09809","repositories_listed":0,"syntology":null},{"url":null,"slug":"randalign-a-parameter-free-method-for","title":"RandAlign: A Parameter-Free Method for Regularizing Graph Convolutional Networks","date":"2024-04-15","arxiv_id":"2404.09774","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-networks-for-binary-programming","title":"Graph Neural Networks for Binary Programming","date":"2024-04-07","arxiv_id":"2404.04874","repositories_listed":0,"syntology":null},{"url":null,"slug":"heteromile-a-multi-level-graph-representation","title":"HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs","date":"2024-03-31","arxiv_id":"2404.00816","repositories_listed":0,"syntology":null},{"url":null,"slug":"dealing-with-missing-modalities-in-multimodal","title":"Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach","date":"2024-03-28","arxiv_id":"2403.19841","repositories_listed":0,"syntology":null}],"record_sha256":"c085db5fbaec2103c0e233c41f8121f1543e427b1272093c28a807528319478f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}