{"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/link-prediction/papers/11","list_of":"/task/link-prediction","task":"Link Prediction","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":11,"pages_in_order":20,"rows_per_page":100,"rows":[1001,1100],"of":1949,"counts":{"archive_papers_tagged":1949,"with_a_code_link":974,"where_syntology_ran_a_sample":233,"not_listed_spam_title":0,"listed":1949,"listed_where_code_ran":233,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":206,"every_run_a_failure_of_syntologys_instrument":27,"listed_with_a_run_with_no_instrument_failure":206,"listed_every_run_a_failure_of_syntologys_instrument":27,"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/link-prediction","prev":"/task/link-prediction/papers/10","next":"/task/link-prediction/papers/12","papers":[{"url":null,"slug":"flash-flexible-learning-of-adaptive-sampling","title":"FLASH: Flexible Learning of Adaptive Sampling from History in Temporal Graph Neural Networks","date":"2025-04-09","arxiv_id":"2504.07337","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-metadata-representation-models","title":"Comparison of Metadata Representation Models for Knowledge Graph Embeddings","date":"2025-03-25","arxiv_id":"2503.21804","repositories_listed":0,"syntology":null},{"url":null,"slug":"unifying-text-semantics-and-graph-structures","title":"Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models","date":"2025-03-18","arxiv_id":"2503.14411","repositories_listed":0,"syntology":null},{"url":null,"slug":"bicliqueencoder-an-efficient-method-for-link","title":"BicliqueEncoder: An Efficient Method for Link Prediction in Bipartite Networks using Formal Concept Analysis and Transformer Encoder","date":"2025-03-06","arxiv_id":"2503.07645","repositories_listed":0,"syntology":null},{"url":null,"slug":"matrix-factorization-for-inferring","title":"Matrix Factorization for Inferring Associations and Missing Links","date":"2025-03-06","arxiv_id":"2503.04680","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-signed-two-space-proximity-model-for","title":"The Signed Two-Space Proximity Model for Learning Representations in Protein-Protein Interaction Networks","date":"2025-03-05","arxiv_id":"2503.03904","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-attributed-dynamic-network","title":"Unsupervised Attributed Dynamic Network Embedding with Stability Guarantees","date":"2025-03-04","arxiv_id":"2503.02859","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-link-prediction-in-temporal","title":"A Survey of Link Prediction in Temporal Networks","date":"2025-02-28","arxiv_id":"2502.21185","repositories_listed":0,"syntology":null},{"url":null,"slug":"fhge-a-fast-heterogeneous-graph-embedding","title":"FHGE: A Fast Heterogeneous Graph Embedding with Ad-hoc Meta-paths","date":"2025-02-22","arxiv_id":"2502.16281","repositories_listed":0,"syntology":null},{"url":null,"slug":"hetfs-a-method-for-fast-similarity-search","title":"HetFS: A Method for Fast Similarity Search with Ad-hoc Meta-paths on Heterogeneous Information Networks","date":"2025-02-22","arxiv_id":"2502.16288","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-link-prediction-new-perspectives","title":"Evaluating link prediction: New perspectives and recommendations","date":"2025-02-18","arxiv_id":"2502.12777","repositories_listed":0,"syntology":null},{"url":null,"slug":"llm-driven-knowledge-distillation-for-dynamic","title":"LLM-driven Knowledge Distillation for Dynamic Text-Attributed Graphs","date":"2025-02-15","arxiv_id":"2502.10914","repositories_listed":0,"syntology":null},{"url":null,"slug":"z-rex-human-interpretable-gnn-explanations","title":"Z-REx: Human-Interpretable GNN Explanations for Real Estate Recommendations","date":"2025-02-12","arxiv_id":"2503.18001","repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-graph-networks-ugn-a-deep-neural","title":"Unified Graph Networks (UGN): A Deep Neural Framework for Solving Graph Problems","date":"2025-02-11","arxiv_id":"2502.07500","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-networks-at-a-fraction","title":"Graph Neural Networks at a Fraction","date":"2025-02-10","arxiv_id":"2502.06136","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-networks-for-o-ran-mobility","title":"Graph Neural Networks for O-RAN Mobility Management: A Link Prediction Approach","date":"2025-02-04","arxiv_id":"2502.02170","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":"brain-inspired-sparse-training-enables","title":"Brain-inspired sparse training enables Transformers and LLMs to perform as fully connected","date":"2025-01-31","arxiv_id":"2501.19107","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-quantized-representation-for","title":"Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models","date":"2025-01-30","arxiv_id":"2501.18119","repositories_listed":0,"syntology":null},{"url":null,"slug":"heuristic-informed-mixture-of-experts-for","title":"Heuristic-Informed Mixture of Experts for Link Prediction in Multilayer Networks","date":"2025-01-29","arxiv_id":"2501.17557","repositories_listed":0,"syntology":null},{"url":null,"slug":"utilizing-graph-neural-networks-for-effective","title":"Utilizing Graph Neural Networks for Effective Link Prediction in Microservice Architectures","date":"2025-01-25","arxiv_id":"2501.15019","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierpromptlm-a-pure-plm-based-framework-for","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","date":"2025-01-22","arxiv_id":"2501.12857","repositories_listed":0,"syntology":null},{"url":null,"slug":"predict-confidently-predict-right-abstention","title":"Predict Confidently, Predict Right: Abstention in Dynamic Graph Learning","date":"2025-01-14","arxiv_id":"2501.08397","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-contrastive-learning-on-multi-label","title":"Graph Contrastive Learning on Multi-label Classification for Recommendations","date":"2025-01-13","arxiv_id":"2501.06985","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperquery-beyond-binary-link-prediction","title":"HyperQuery: Beyond Binary Link Prediction","date":"2025-01-13","arxiv_id":"2501.07731","repositories_listed":0,"syntology":null},{"url":null,"slug":"coupled-hierarchical-structure-learning-using","title":"Coupled Hierarchical Structure Learning using Tree-Wasserstein Distance","date":"2025-01-07","arxiv_id":"2501.03627","repositories_listed":0,"syntology":null},{"url":null,"slug":"chat-beyond-contrastive-graph-transformer-for","title":"CHAT: Beyond Contrastive Graph Transformer for Link Prediction in Heterogeneous Networks","date":"2025-01-06","arxiv_id":"2501.02760","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-relational-context-perception-for","title":"Efficient Relational Context Perception for Knowledge Graph Completion","date":"2024-12-31","arxiv_id":"2501.00397","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":"extending-twig-zero-shot-predictive","title":"Extending TWIG: Zero-Shot Predictive Hyperparameter Selection for KGEs based on Graph Structure","date":"2024-12-19","arxiv_id":"2412.14801","repositories_listed":0,"syntology":null},{"url":null,"slug":"practicable-black-box-evasion-attacks-on-link","title":"Practicable Black-box Evasion Attacks on Link Prediction in Dynamic Graphs -- A Graph Sequential Embedding Method","date":"2024-12-17","arxiv_id":"2412.13134","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-temporal-link-prediction-with","title":"Enhancing Temporal Link Prediction with HierTKG: A Hierarchical Temporal Knowledge Graph Framework","date":"2024-12-16","arxiv_id":"2412.12385","repositories_listed":0,"syntology":null},{"url":null,"slug":"gnn-applied-to-ego-nets-for-friend","title":"GNN Applied to Ego-nets for Friend Suggestions","date":"2024-12-16","arxiv_id":"2412.11888","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":"edge-contrastive-learning-an-augmentation","title":"Edge Contrastive Learning: An Augmentation-Free Graph Contrastive Learning Model","date":"2024-12-15","arxiv_id":"2412.11075","repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-learning-of-dynamic-networks","title":"Representation learning of dynamic networks","date":"2024-12-15","arxiv_id":"2412.11065","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-knowledge-graph-structure-and","title":"A Survey on Knowledge Graph Structure and Knowledge Graph Embeddings","date":"2024-12-13","arxiv_id":"2412.10092","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-communities-to-interpretable-network-and","title":"From communities to interpretable network and word embedding: an unified approach","date":"2024-12-11","arxiv_id":"2412.08187","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-node-embeddings-for-graph","title":"Multi-Scale Node Embeddings for Graph Modeling and Generation","date":"2024-12-05","arxiv_id":"2412.04354","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-supply-chain-visibility-with-1","title":"Enhancing Supply Chain Visibility with Generative AI: An Exploratory Case Study on Relationship Prediction in Knowledge Graphs","date":"2024-12-04","arxiv_id":"2412.03390","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-the-use-of-feature-selection-methods","title":"How the use of feature selection methods influences the efficiency and accuracy of complex network simulations","date":"2024-12-02","arxiv_id":"2412.01096","repositories_listed":0,"syntology":null},{"url":null,"slug":"learn-to-unlearn-meta-learning-based","title":"Learn to Unlearn: Meta-Learning-Based Knowledge Graph Embedding Unlearning","date":"2024-12-01","arxiv_id":"2412.00881","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-model-for-one-graph-a-new-perspective-for","title":"One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs","date":"2024-11-30","arxiv_id":"2412.00315","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":"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":"deep-sparse-latent-feature-models-for","title":"Deep Sparse Latent Feature Models for Knowledge Graph Completion","date":"2024-11-24","arxiv_id":"2411.15694","repositories_listed":0,"syntology":null},{"url":null,"slug":"visgraphvar-a-benchmark-generator-for","title":"VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models","date":"2024-11-22","arxiv_id":"2411.14832","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":"scalable-deep-metric-learning-on-attributed","title":"Scalable Deep Metric Learning on Attributed Graphs","date":"2024-11-20","arxiv_id":"2411.13014","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-graph-structured-edge-partition","title":"Hierarchical-Graph-Structured Edge Partition Models for Learning Evolving Community Structure","date":"2024-11-18","arxiv_id":"2411.11536","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-link-prediction-with-fuzzy-graph","title":"Enhancing Link Prediction with Fuzzy Graph Attention Networks and Dynamic Negative Sampling","date":"2024-11-12","arxiv_id":"2411.07482","repositories_listed":0,"syntology":null},{"url":"/paper/mocokgc-momentum-contrast-entity-encoding-for","slug":"mocokgc-momentum-contrast-entity-encoding-for","title":"MoCoKGC: Momentum Contrast Entity Encoding for Knowledge Graph Completion","date":"2024-11-12","arxiv_id":null,"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":"yoso-you-only-sample-once-via-compressed","title":"YOSO: You-Only-Sample-Once via Compressed Sensing for Graph Neural Network Training","date":"2024-11-08","arxiv_id":"2411.05693","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":"capturing-and-anticipating-user-intents-in","title":"Capturing and Anticipating User Intents in Data Analytics via Knowledge Graphs","date":"2024-11-01","arxiv_id":"2411.01023","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-bridge-spatial-and-temporal","title":"How to Bridge Spatial and Temporal Heterogeneity in Link Prediction? A Contrastive Method","date":"2024-11-01","arxiv_id":"2411.00612","repositories_listed":0,"syntology":null},{"url":null,"slug":"just-propagate-unifying-matrix-factorization","title":"Just Propagate: Unifying Matrix Factorization, Network Embedding, and LightGCN for Link Prediction","date":"2024-10-26","arxiv_id":"2410.21325","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":"gene-metabolite-association-prediction-with","title":"Gene-Metabolite Association Prediction with Interactive Knowledge Transfer Enhanced Graph for Metabolite Production","date":"2024-10-24","arxiv_id":"2410.18475","repositories_listed":0,"syntology":null},{"url":null,"slug":"geometric-feature-enhanced-knowledge-graph","title":"Geometric Feature Enhanced Knowledge Graph Embedding and Spatial Reasoning","date":"2024-10-24","arxiv_id":"2410.18345","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-frame-detection-via-graph-neural","title":"Multi-frame Detection via Graph Neural Networks: A Link Prediction Approach","date":"2024-10-17","arxiv_id":"2410.13436","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-complex-query-answering-really-complex","title":"Is Complex Query Answering Really Complex?","date":"2024-10-16","arxiv_id":"2410.12537","repositories_listed":0,"syntology":null},{"url":null,"slug":"parametric-graph-representations-in-the-era","title":"What Do LLMs Need to Understand Graphs: A Survey of Parametric Representation of Graphs","date":"2024-10-16","arxiv_id":"2410.12126","repositories_listed":0,"syntology":null},{"url":null,"slug":"inference-over-unseen-entities-relations-and","title":"Inference over Unseen Entities, Relations and Literals on Knowledge Graphs","date":"2024-10-09","arxiv_id":"2410.06742","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-graph-neural-networks-meet-dynamic-mode","title":"When Graph Neural Networks Meet Dynamic Mode Decomposition","date":"2024-10-08","arxiv_id":"2410.05593","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-node-representation-by-boosting","title":"Improving Node Representation by Boosting Target-Aware Contrastive Loss","date":"2024-10-04","arxiv_id":"2410.03901","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":"stabilizing-the-kumaraswamy-distribution","title":"Stabilizing the Kumaraswamy Distribution","date":"2024-10-01","arxiv_id":"2410.00660","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":"pre-trained-graphformer-based-ranking-at-web","title":"Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)","date":"2024-09-25","arxiv_id":"2409.16590","repositories_listed":0,"syntology":null},{"url":null,"slug":"motifdisco-motif-causal-discovery-for-time","title":"MotifDisco: Motif Causal Discovery For Time Series Motifs","date":"2024-09-23","arxiv_id":"2409.15219","repositories_listed":0,"syntology":null},{"url":null,"slug":"signed-graph-autoencoder-for-explainable-and","title":"Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings","date":"2024-09-16","arxiv_id":"2409.10452","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-link-and-flow-prediction-in-bank","title":"Dynamic Link and Flow Prediction in Bank Transfer Networks","date":"2024-09-13","arxiv_id":"2409.08718","repositories_listed":0,"syntology":null},{"url":null,"slug":"promoting-fairness-in-link-prediction-with","title":"Promoting Fairness in Link Prediction with Graph Enhancement","date":"2024-09-13","arxiv_id":"2409.08658","repositories_listed":0,"syntology":null},{"url":null,"slug":"sub-graph-based-diffusion-model-for-link","title":"Sub-graph Based Diffusion Model for Link Prediction","date":"2024-09-13","arxiv_id":"2409.08487","repositories_listed":0,"syntology":null},{"url":null,"slug":"hypercausallp-causal-link-prediction-using","title":"HyperCausalLP: Causal Link Prediction using Hyper-Relational Knowledge Graph","date":"2024-09-12","arxiv_id":"2410.14679","repositories_listed":0,"syntology":null},{"url":null,"slug":"influence-of-backdoor-paths-on-causal-link","title":"Influence of Backdoor Paths on Causal Link Prediction","date":"2024-09-12","arxiv_id":"2410.14680","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-graph-based-foundation-model-for","title":"Towards a graph-based foundation model for network traffic analysis","date":"2024-09-12","arxiv_id":"2409.08111","repositories_listed":0,"syntology":null},{"url":null,"slug":"dot-product-is-all-you-need-bridging-the-gap","title":"Dot Product is All You Need: Bridging the Gap Between Item Recommendation and Link Prediction","date":"2024-09-11","arxiv_id":"2409.07433","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-signed-graph-embedding-methods","title":"A Survey on Signed Graph Embedding: Methods and Applications","date":"2024-09-05","arxiv_id":"2409.03916","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-attention-inference-of-network-topology","title":"Graph Attention Inference of Network Topology in Multi-Agent Systems","date":"2024-08-27","arxiv_id":"2408.15449","repositories_listed":0,"syntology":null},{"url":null,"slug":"rocp-gnn-robust-conformal-prediction-for","title":"RoCP-GNN: Robust Conformal Prediction for Graph Neural Networks in Node-Classification","date":"2024-08-25","arxiv_id":"2408.13825","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-expressivity-in-graph-neural","title":"Enhanced Expressivity in Graph Neural Networks with Lanczos-Based Linear Constraints","date":"2024-08-22","arxiv_id":"2408.12334","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-large-language-models-capabilities","title":"GS-KGC: A Generative Subgraph-based Framework for Knowledge Graph Completion with Large Language Models","date":"2024-08-20","arxiv_id":"2408.10819","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":"conformalized-answer-set-prediction-for","title":"Conformalized Answer Set Prediction for Knowledge Graph Embedding","date":"2024-08-15","arxiv_id":"2408.08248","repositories_listed":0,"syntology":null},{"url":null,"slug":"predictive-multiplicity-of-knowledge-graph","title":"Predictive Multiplicity of Knowledge Graph Embeddings in Link Prediction","date":"2024-08-15","arxiv_id":"2408.08226","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":"fast-and-frugal-text-graph-transformers-are","title":"Fast-and-Frugal Text-Graph Transformers are Effective Link Predictors","date":"2024-08-13","arxiv_id":"2408.06778","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":"dygmamba-efficiently-modeling-long-term","title":"DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models","date":"2024-08-08","arxiv_id":"2408.04713","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":"2408-02707","title":"SnapE -- Training Snapshot Ensembles of Link Prediction Models","date":"2024-08-05","arxiv_id":"2408.02707","repositories_listed":0,"syntology":null},{"url":null,"slug":"co-neighbor-encoding-schema-a-light-cost","title":"Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction","date":"2024-07-30","arxiv_id":"2407.20871","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":"optimizing-long-tailed-link-prediction-in","title":"Optimizing Long-tailed Link Prediction in Graph Neural Networks through Structure Representation Enhancement","date":"2024-07-30","arxiv_id":"2407.20499","repositories_listed":0,"syntology":null}],"record_sha256":"d63ddc8b9afb541f390194f2b126172ca5bb4462d8e3ce560762120dc7f2a07e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}