{"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/2","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":2,"pages_in_order":16,"rows_per_page":100,"rows":[101,200],"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","next":"/task/graph-learning/papers/3","papers":[{"url":"/paper/masked-language-models-are-good-heterogeneous","slug":"masked-language-models-are-good-heterogeneous","title":"Masked Language Models are Good Heterogeneous Graph Generalizers","date":"2025-06-06","arxiv_id":"2506.06157","repositories_listed":1,"syntology":null},{"url":"/paper/soc-dgl-social-interaction-behavior-inspired","slug":"soc-dgl-social-interaction-behavior-inspired","title":"SOC-DGL: Social Interaction Behavior Inspired Dual Graph Learning Framework for Drug-Target Interaction Identification","date":"2025-06-02","arxiv_id":"2506.01405","repositories_listed":1,"syntology":null},{"url":"/paper/cellclat-preserving-topology-and-trimming","slug":"cellclat-preserving-topology-and-trimming","title":"CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning","date":"2025-05-27","arxiv_id":"2505.21587","repositories_listed":1,"syntology":null},{"url":"/paper/learning-individual-behavior-in-agent-based","slug":"learning-individual-behavior-in-agent-based","title":"Learning Individual Behavior in Agent-Based Models with Graph Diffusion Networks","date":"2025-05-27","arxiv_id":"2505.21426","repositories_listed":1,"syntology":null},{"url":"/paper/using-large-language-models-to-tackle","slug":"using-large-language-models-to-tackle","title":"Using Large Language Models to Tackle Fundamental Challenges in Graph Learning: A Comprehensive Survey","date":"2025-05-24","arxiv_id":"2505.18475","repositories_listed":1,"syntology":null},{"url":"/paper/scalable-graph-generative-modeling-via","slug":"scalable-graph-generative-modeling-via","title":"Scalable Graph Generative Modeling via Substructure Sequences","date":"2025-05-22","arxiv_id":"2505.16130","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/scalable-graph-generative-modeling-via#ran","syntology_url":"https://syntology.ai/paper/2505.16130","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.16130"}},"official":{"repos":["zehong-wang/g2pm"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-foundation-models-a-comprehensive","slug":"graph-foundation-models-a-comprehensive","title":"Graph Foundation Models: A Comprehensive Survey","date":"2025-05-21","arxiv_id":"2505.15116","repositories_listed":1,"syntology":null},{"url":"/paper/lightweight-transformer-via-unrolling-of","slug":"lightweight-transformer-via-unrolling-of","title":"Lightweight Transformer via Unrolling of Mixed Graph Algorithms for Traffic Forecast","date":"2025-05-19","arxiv_id":"2505.13102","repositories_listed":1,"syntology":null},{"url":"/paper/learn-to-think-bootstrapping-llm-reasoning","slug":"learn-to-think-bootstrapping-llm-reasoning","title":"Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Learning","date":"2025-05-09","arxiv_id":"2505.06321","repositories_listed":1,"syntology":null},{"url":"/paper/partial-label-clustering","slug":"partial-label-clustering","title":"Partial Label Clustering","date":"2025-05-06","arxiv_id":"2505.03207","repositories_listed":1,"syntology":null},{"url":"/paper/graphatc-advancing-multilevel-and-multi-label","slug":"graphatc-advancing-multilevel-and-multi-label","title":"GraphATC: advancing multilevel and multi-label anatomical therapeutic chemical classification via atom-level graph learning","date":"2025-04-26","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/graph-learning-at-scale-characterizing-and","slug":"graph-learning-at-scale-characterizing-and","title":"Graph Learning at Scale: Characterizing and Optimizing Pre-Propagation GNNs","date":"2025-04-17","arxiv_id":"2504.13266","repositories_listed":1,"syntology":null},{"url":"/paper/gt-svq-a-linear-time-graph-transformer-for","slug":"gt-svq-a-linear-time-graph-transformer-for","title":"GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector Quantization","date":"2025-04-16","arxiv_id":"2504.11840","repositories_listed":1,"syntology":null},{"url":"/paper/trajectory-encoding-temporal-graph-networks","slug":"trajectory-encoding-temporal-graph-networks","title":"Trajectory Encoding Temporal Graph Networks","date":"2025-04-15","arxiv_id":"2504.11386","repositories_listed":1,"syntology":null},{"url":"/paper/nettag-a-multimodal-rtl-and-layout-aligned","slug":"nettag-a-multimodal-rtl-and-layout-aligned","title":"NetTAG: A Multimodal RTL-and-Layout-Aligned Netlist Foundation Model via Text-Attributed Graph","date":"2025-04-12","arxiv_id":"2504.09260","repositories_listed":1,"syntology":null},{"url":"/paper/between-linear-and-sinusoidal-rethinking-the","slug":"between-linear-and-sinusoidal-rethinking-the","title":"Between Linear and Sinusoidal: Rethinking the Time Encoder in Dynamic Graph Learning","date":"2025-04-10","arxiv_id":"2504.08129","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"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) · 2 unverified","sample_list":"/paper/between-linear-and-sinusoidal-rethinking-the#ran","syntology_url":"https://syntology.ai/paper/2504.08129","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.08129"}},"official":{"repos":["hsinghuan/dg-linear-time"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/scalable-hypergraph-structure-learning-with","slug":"scalable-hypergraph-structure-learning-with","title":"Scalable Hypergraph Structure Learning with Diverse Smoothness Priors","date":"2025-04-04","arxiv_id":"2504.03583","repositories_listed":1,"syntology":null},{"url":"/paper/advances-in-continual-graph-learning-for-anti","slug":"advances-in-continual-graph-learning-for-anti","title":"Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review","date":"2025-03-31","arxiv_id":"2503.24259","repositories_listed":1,"syntology":null},{"url":"/paper/lorentzian-graph-isomorphic-network","slug":"lorentzian-graph-isomorphic-network","title":"LGIN: Defining an Approximately Powerful Hyperbolic GNN","date":"2025-03-31","arxiv_id":"2504.00142","repositories_listed":1,"syntology":null},{"url":"/paper/advsgm-differentially-private-graph-learning","slug":"advsgm-differentially-private-graph-learning","title":"AdvSGM: Differentially Private Graph Learning via Adversarial Skip-gram Model","date":"2025-03-27","arxiv_id":"2503.21426","repositories_listed":1,"syntology":null},{"url":"/paper/2503-19314","slug":"2503-19314","title":"RGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on Graphs","date":"2025-03-25","arxiv_id":"2503.19314","repositories_listed":1,"syntology":null},{"url":"/paper/nafm-pre-training-a-foundation-model-for","slug":"nafm-pre-training-a-foundation-model-for","title":"NaFM: Pre-training a Foundation Model for Small-Molecule Natural Products","date":"2025-03-22","arxiv_id":"2503.17656","repositories_listed":1,"syntology":null},{"url":"/paper/network-wide-freeway-traffic-estimation-using","slug":"network-wide-freeway-traffic-estimation-using","title":"Network-wide Freeway Traffic Estimation Using Sparse Sensor Data: A Dirichlet Graph Auto-Encoder Approach","date":"2025-03-20","arxiv_id":"2503.15845","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-graph-anomaly-detection-via","slug":"unsupervised-graph-anomaly-detection-via","title":"Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph Learning","date":"2025-03-15","arxiv_id":"2503.12037","repositories_listed":1,"syntology":null},{"url":"/paper/a-survey-of-cross-domain-graph-learning","slug":"a-survey-of-cross-domain-graph-learning","title":"A Survey of Cross-domain Graph Learning: Progress and Future Directions","date":"2025-03-14","arxiv_id":"2503.11086","repositories_listed":1,"syntology":null},{"url":"/paper/hegmn-heterogeneous-graph-matching-network","slug":"hegmn-heterogeneous-graph-matching-network","title":"HeGMN: Heterogeneous Graph Matching Network for Learning Graph Similarity","date":"2025-03-11","arxiv_id":"2503.08739","repositories_listed":1,"syntology":null},{"url":"/paper/traffickan-gcn-graph-convolutional-based","slug":"traffickan-gcn-graph-convolutional-based","title":"TrafficKAN-GCN: Graph Convolutional-based Kolmogorov-Arnold Network for Traffic Flow Optimization","date":"2025-03-05","arxiv_id":"2503.03276","repositories_listed":1,"syntology":null},{"url":"/paper/fairness-and-or-privacy-on-social-graphs","slug":"fairness-and-or-privacy-on-social-graphs","title":"Fairness and/or Privacy on Social Graphs","date":"2025-03-03","arxiv_id":"2503.02114","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-graph-tasks-with-pure-llms-a","slug":"exploring-graph-tasks-with-pure-llms-a","title":"Exploring Graph Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation","date":"2025-02-26","arxiv_id":"2502.18771","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/exploring-graph-tasks-with-pure-llms-a#ran","syntology_url":"https://syntology.ai/paper/2502.18771","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.18771"}},"official":{"repos":["myflashbarry/LLM-benchmarking"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/decoupled-graph-energy-based-model-for-node","slug":"decoupled-graph-energy-based-model-for-node","title":"Decoupled Graph Energy-based Model for Node Out-of-Distribution Detection on Heterophilic Graphs","date":"2025-02-25","arxiv_id":"2502.17912","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/decoupled-graph-energy-based-model-for-node#ran","syntology_url":"https://syntology.ai/paper/2502.17912","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.17912"}},"official":{"repos":["draym28/degem"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/task-graph-maximum-likelihood-estimation-for","slug":"task-graph-maximum-likelihood-estimation-for","title":"Task Graph Maximum Likelihood Estimation for Procedural Activity Understanding in Egocentric Videos","date":"2025-02-25","arxiv_id":"2502.17753","repositories_listed":1,"syntology":null},{"url":"/paper/gigl-large-scale-graph-neural-networks-at","slug":"gigl-large-scale-graph-neural-networks-at","title":"GiGL: Large-Scale Graph Neural Networks at Snapchat","date":"2025-02-20","arxiv_id":"2502.15054","repositories_listed":1,"syntology":null},{"url":"/paper/democratizing-large-language-model-based","slug":"democratizing-large-language-model-based","title":"Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge Graphs","date":"2025-02-19","arxiv_id":"2502.13555","repositories_listed":1,"syntology":null},{"url":"/paper/dual-level-mixup-for-graph-few-shot-learning","slug":"dual-level-mixup-for-graph-few-shot-learning","title":"Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks","date":"2025-02-19","arxiv_id":"2502.14158","repositories_listed":1,"syntology":null},{"url":"/paper/model-generalization-on-text-attribute-graphs","slug":"model-generalization-on-text-attribute-graphs","title":"Model Generalization on Text Attribute Graphs: Principles with Large Language Models","date":"2025-02-17","arxiv_id":"2502.11836","repositories_listed":1,"syntology":null},{"url":"/paper/graph-diffusion-network-for-drug-gene","slug":"graph-diffusion-network-for-drug-gene","title":"Graph Diffusion Network for Drug-Gene Prediction","date":"2025-02-13","arxiv_id":"2502.09335","repositories_listed":1,"syntology":null},{"url":"/paper/simple-path-structural-encoding-for-graph","slug":"simple-path-structural-encoding-for-graph","title":"Simple Path Structural Encoding for Graph Transformers","date":"2025-02-13","arxiv_id":"2502.09365","repositories_listed":1,"syntology":null},{"url":"/paper/prompt-driven-continual-graph-learning","slug":"prompt-driven-continual-graph-learning","title":"Prompt-Driven Continual Graph Learning","date":"2025-02-10","arxiv_id":"2502.06327","repositories_listed":1,"syntology":null},{"url":"/paper/robust-graph-learning-against-adversarial","slug":"robust-graph-learning-against-adversarial","title":"Robust Graph Learning Against Adversarial Evasion Attacks via Prior-Free Diffusion-Based Structure Purification","date":"2025-02-07","arxiv_id":"2502.05000","repositories_listed":1,"syntology":null},{"url":"/paper/medgnn-towards-multi-resolution","slug":"medgnn-towards-multi-resolution","title":"MedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification","date":"2025-02-06","arxiv_id":"2502.04515","repositories_listed":1,"syntology":null},{"url":"/paper/no-metric-to-rule-them-all-toward-principled","slug":"no-metric-to-rule-them-all-toward-principled","title":"No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets","date":"2025-02-04","arxiv_id":"2502.02379","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":4,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/no-metric-to-rule-them-all-toward-principled#ran","syntology_url":"https://syntology.ai/paper/2502.02379","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.02379"}},"official":{"repos":["aidos-lab/rings"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/random-walk-guided-hyperbolic-graph","slug":"random-walk-guided-hyperbolic-graph","title":"Random Walk Guided Hyperbolic Graph Distillation","date":"2025-01-26","arxiv_id":"2501.15696","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-invariant-learning-framework-for","slug":"a-unified-invariant-learning-framework-for","title":"A Unified Invariant Learning Framework for Graph Classification","date":"2025-01-22","arxiv_id":"2501.12595","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-spatiotemporal-augmentation-for","slug":"adaptive-spatiotemporal-augmentation-for","title":"Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning","date":"2025-01-17","arxiv_id":"2501.10010","repositories_listed":1,"syntology":null},{"url":"/paper/topology-driven-attribute-recovery-for","slug":"topology-driven-attribute-recovery-for","title":"Topology-Driven Attribute Recovery for Attribute Missing Graph Learning in Social Internet of Things","date":"2025-01-17","arxiv_id":"2501.10151","repositories_listed":1,"syntology":null},{"url":"/paper/a-simple-graph-contrastive-learning-framework","slug":"a-simple-graph-contrastive-learning-framework","title":"A Simple Graph Contrastive Learning Framework for Short Text Classification","date":"2025-01-16","arxiv_id":"2501.09219","repositories_listed":1,"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":1,"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/a-simple-graph-contrastive-learning-framework#ran","syntology_url":"https://syntology.ai/paper/2501.09219","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.09219"}},"official":{"repos":["keaml-jlu/simstc"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/boosting-short-text-classification-with-multi","slug":"boosting-short-text-classification-with-multi","title":"Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning","date":"2025-01-16","arxiv_id":"2501.09214","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":2,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"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) · 0 unverified","sample_list":"/paper/boosting-short-text-classification-with-multi#ran","syntology_url":"https://syntology.ai/paper/2501.09214","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.09214"}},"official":{"repos":["keaml-jlu/mi-delight"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-graph-representation-learning-with-1","slug":"enhancing-graph-representation-learning-with-1","title":"Enhancing Graph Representation Learning with Localized Topological Features","date":"2025-01-15","arxiv_id":"2501.09178","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/enhancing-graph-representation-learning-with-1#ran","syntology_url":"https://syntology.ai/paper/2501.09178","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.09178"}},"official":{"repos":["pkuyzy/TLC-GNN"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-based-multimodal-and-multi-view","slug":"graph-based-multimodal-and-multi-view","title":"Graph-Based Multimodal and Multi-view Alignment for Keystep Recognition","date":"2025-01-07","arxiv_id":"2501.04121","repositories_listed":1,"syntology":null},{"url":"/paper/structure-preference-enabled-graph-embedding","slug":"structure-preference-enabled-graph-embedding","title":"Structure-Preference Enabled Graph Embedding Generation under Differential Privacy","date":"2025-01-07","arxiv_id":"2501.03451","repositories_listed":1,"syntology":null},{"url":"/paper/long-range-brain-graph-transformer","slug":"long-range-brain-graph-transformer","title":"Long-range Brain Graph Transformer","date":"2025-01-02","arxiv_id":"2501.01100","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/long-range-brain-graph-transformer#ran","syntology_url":"https://syntology.ai/paper/2501.01100","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.01100"}},"official":{"repos":["yushuowiki/alter"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/attrireboost-a-gradient-free-propagation","slug":"attrireboost-a-gradient-free-propagation","title":"AttriReBoost: A Gradient-Free Propagation Optimization Method for Cold Start Mitigation in Attribute Missing Graphs","date":"2025-01-01","arxiv_id":"2501.00743","repositories_listed":1,"syntology":null},{"url":"/paper/virtual-nodes-can-help-tackling-distribution","slug":"virtual-nodes-can-help-tackling-distribution","title":"Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning","date":"2024-12-26","arxiv_id":"2412.19229","repositories_listed":1,"syntology":null},{"url":"/paper/noisehgnn-synthesized-similarity-graph-based","slug":"noisehgnn-synthesized-similarity-graph-based","title":"NoiseHGNN: Synthesized Similarity Graph-Based Neural Network For Noised Heterogeneous Graph Representation Learning","date":"2024-12-24","arxiv_id":"2412.18267","repositories_listed":1,"syntology":null},{"url":"/paper/graphseqlm-a-unified-graph-language-framework","slug":"graphseqlm-a-unified-graph-language-framework","title":"GraphSeqLM: A Unified Graph Language Framework for Omic Graph Learning","date":"2024-12-20","arxiv_id":"2412.15790","repositories_listed":1,"syntology":null},{"url":"/paper/spectrum-based-modality-representation-fusion","slug":"spectrum-based-modality-representation-fusion","title":"Spectrum-based Modality Representation Fusion Graph Convolutional Network for Multimodal Recommendation","date":"2024-12-19","arxiv_id":"2412.14978","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-and-improving-large-vision","slug":"benchmarking-and-improving-large-vision","title":"Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and Reasoning","date":"2024-12-18","arxiv_id":"2412.13540","repositories_listed":1,"syntology":null},{"url":"/paper/modality-independent-graph-neural-networks","slug":"modality-independent-graph-neural-networks","title":"Modality-Independent Graph Neural Networks with Global Transformers for Multimodal Recommendation","date":"2024-12-18","arxiv_id":"2412.13994","repositories_listed":1,"syntology":null},{"url":"/paper/spgl-enhancing-session-based-recommendation","slug":"spgl-enhancing-session-based-recommendation","title":"SPGL: Enhancing Session-based Recommendation with Single Positive Graph Learning","date":"2024-12-16","arxiv_id":"2412.11846","repositories_listed":1,"syntology":null},{"url":"/paper/semi-implicit-neural-ordinary-differential","slug":"semi-implicit-neural-ordinary-differential","title":"Semi-Implicit Neural Ordinary Differential Equations","date":"2024-12-15","arxiv_id":"2412.11301","repositories_listed":1,"syntology":null},{"url":"/paper/mopi-hfrs-a-multi-objective-personalized","slug":"mopi-hfrs-a-multi-objective-personalized","title":"MOPI-HFRS: A Multi-objective Personalized Health-aware Food Recommendation System with LLM-enhanced Interpretation","date":"2024-12-12","arxiv_id":"2412.08847","repositories_listed":1,"syntology":null},{"url":"/paper/multi-scale-heterogeneous-text-attributed","slug":"multi-scale-heterogeneous-text-attributed","title":"Multi-Scale Heterogeneous Text-Attributed Graph Datasets From Diverse Domains","date":"2024-12-12","arxiv_id":"2412.08937","repositories_listed":1,"syntology":null},{"url":"/paper/gll-a-differentiable-graph-learning-layer-for","slug":"gll-a-differentiable-graph-learning-layer-for","title":"GLL: A Differentiable Graph Learning Layer for Neural Networks","date":"2024-12-11","arxiv_id":"2412.08016","repositories_listed":1,"syntology":null},{"url":"/paper/fast-track-to-winning-tickets-repowering-one","slug":"fast-track-to-winning-tickets-repowering-one","title":"Fast Track to Winning Tickets: Repowering One-Shot Pruning for Graph Neural Networks","date":"2024-12-10","arxiv_id":"2412.07605","repositories_listed":1,"syntology":null},{"url":"/paper/pix2poly-a-sequence-prediction-method-for-end","slug":"pix2poly-a-sequence-prediction-method-for-end","title":"Pix2Poly: A Sequence Prediction Method for End-to-end Polygonal Building Footprint Extraction from Remote Sensing Imagery","date":"2024-12-10","arxiv_id":"2412.07899","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-graph-representation-with-contrastive","slug":"dynamic-graph-representation-with-contrastive","title":"Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static Relations","date":"2024-12-05","arxiv_id":"2412.04034","repositories_listed":1,"syntology":null},{"url":"/paper/training-mlps-on-graphs-without-supervision","slug":"training-mlps-on-graphs-without-supervision","title":"Training MLPs on Graphs without Supervision","date":"2024-12-05","arxiv_id":"2412.03864","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/training-mlps-on-graphs-without-supervision#ran","syntology_url":"https://syntology.ai/paper/2412.03864","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.03864"}},"official":{"repos":["zehong-wang/simmlp"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/revisiting-self-supervised-heterogeneous","slug":"revisiting-self-supervised-heterogeneous","title":"Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective","date":"2024-12-01","arxiv_id":"2412.00742","repositories_listed":1,"syntology":null},{"url":"/paper/multigraph-message-passing-with-bi","slug":"multigraph-message-passing-with-bi","title":"Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations","date":"2024-11-29","arxiv_id":"2412.00241","repositories_listed":1,"syntology":null},{"url":"/paper/federated-continual-graph-learning","slug":"federated-continual-graph-learning","title":"Federated Continual Graph Learning","date":"2024-11-28","arxiv_id":"2411.18919","repositories_listed":1,"syntology":null},{"url":"/paper/scale-invariance-of-graph-neural-networks","slug":"scale-invariance-of-graph-neural-networks","title":"Scale Invariance of Graph Neural Networks","date":"2024-11-28","arxiv_id":"2411.19392","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-multi-graph-learning-with","slug":"contrastive-multi-graph-learning-with","title":"Contrastive Multi-graph Learning with Neighbor Hierarchical Sifting for Semi-supervised Text Classification","date":"2024-11-25","arxiv_id":"2411.16787","repositories_listed":1,"syntology":null},{"url":"/paper/haar-laplacian-for-directed-graphs","slug":"haar-laplacian-for-directed-graphs","title":"Haar-Laplacian for directed graphs","date":"2024-11-23","arxiv_id":"2411.15527","repositories_listed":1,"syntology":null},{"url":"/paper/teaching-mlps-to-master-heterogeneous-graph","slug":"teaching-mlps-to-master-heterogeneous-graph","title":"Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference","date":"2024-11-21","arxiv_id":"2411.14035","repositories_listed":1,"syntology":null},{"url":"/paper/scalenet-scale-invariance-learning-in","slug":"scalenet-scale-invariance-learning-in","title":"ScaleNet: Scale Invariance Learning in Directed Graphs","date":"2024-11-13","arxiv_id":"2411.08758","repositories_listed":1,"syntology":null},{"url":"/paper/an-efficient-memory-module-for-graph-few-shot","slug":"an-efficient-memory-module-for-graph-few-shot","title":"An Efficient Memory Module for Graph Few-Shot Class-Incremental Learning","date":"2024-11-11","arxiv_id":"2411.06659","repositories_listed":1,"syntology":null},{"url":"/paper/gft-graph-foundation-model-with-transferable","slug":"gft-graph-foundation-model-with-transferable","title":"GFT: Graph Foundation Model with Transferable Tree Vocabulary","date":"2024-11-09","arxiv_id":"2411.06070","repositories_listed":1,"syntology":{"n":25,"n_ran":18,"n_constructed":0,"n_ran_checked":10,"n_instrument":8,"n_unverified":7,"n_honours":1,"n_violates":2,"n_no_contract":7,"n_pointer_only":10,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 2 violated, 7 with no contract checked; 8 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/gft-graph-foundation-model-with-transferable#ran","syntology_url":"https://syntology.ai/paper/2411.06070","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.06070"}},"official":{"repos":["zehong-wang/gft"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":7,"ran_from_kinds":["community","official"]}}},{"url":"/paper/distributed-order-fractional-graph-operating","slug":"distributed-order-fractional-graph-operating","title":"Distributed-Order Fractional Graph Operating Network","date":"2024-11-08","arxiv_id":"2411.05274","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":8,"phrase":"8 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/distributed-order-fractional-graph-operating#ran","syntology_url":"https://syntology.ai/paper/2411.05274","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.05274"}},"official":{"repos":["zknus/neurips-2024-dragon"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/query-efficient-adversarial-attack-against","slug":"query-efficient-adversarial-attack-against","title":"Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning","date":"2024-11-05","arxiv_id":"2411.02809","repositories_listed":1,"syntology":null},{"url":"/paper/elu-gcn-effectively-label-utilizing-graph","slug":"elu-gcn-effectively-label-utilizing-graph","title":"Enhancing the Influence of Labels on Unlabeled Nodes in Graph Convolutional Networks","date":"2024-11-04","arxiv_id":"2411.02279","repositories_listed":1,"syntology":null},{"url":"/paper/pagerank-bandits-for-link-prediction","slug":"pagerank-bandits-for-link-prediction","title":"PageRank Bandits for Link Prediction","date":"2024-11-03","arxiv_id":"2411.01410","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pagerank-bandits-for-link-prediction#ran","syntology_url":"https://syntology.ai/paper/2411.01410","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.01410"}},"official":{"repos":["jiaruzouu/prb"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-learning-for-numeric-planning","slug":"graph-learning-for-numeric-planning","title":"Graph Learning for Numeric Planning","date":"2024-10-31","arxiv_id":"2410.24080","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 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; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/graph-learning-for-numeric-planning#ran","syntology_url":"https://syntology.ai/paper/2410.24080","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.24080"}},"official":{"repos":["DillonZChen/goose"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/ragraph-a-general-retrieval-augmented-graph","slug":"ragraph-a-general-retrieval-augmented-graph","title":"RAGraph: A General Retrieval-Augmented Graph Learning Framework","date":"2024-10-31","arxiv_id":"2410.23855","repositories_listed":1,"syntology":null},{"url":"/paper/fedssp-federated-graph-learning-with-spectral","slug":"fedssp-federated-graph-learning-with-spectral","title":"FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference","date":"2024-10-26","arxiv_id":"2410.20105","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/fedssp-federated-graph-learning-with-spectral#ran","syntology_url":"https://syntology.ai/paper/2410.20105","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.20105"}},"official":{"repos":["oakleytan/fedssp"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/homomorphism-counts-as-structural-encodings","slug":"homomorphism-counts-as-structural-encodings","title":"Homomorphism Counts as Structural Encodings for Graph Learning","date":"2024-10-24","arxiv_id":"2410.18676","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/homomorphism-counts-as-structural-encodings#ran","syntology_url":"https://syntology.ai/paper/2410.18676","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.18676"}},"official":{"repos":["linusbao/MoSE"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/missnodag-differentiable-cyclic-causal-graph","slug":"missnodag-differentiable-cyclic-causal-graph","title":"MissNODAG: Differentiable Cyclic Causal Graph Learning from Incomplete Data","date":"2024-10-24","arxiv_id":"2410.18918","repositories_listed":1,"syntology":null},{"url":"/paper/disengcd-a-meta-multigraph-assisted","slug":"disengcd-a-meta-multigraph-assisted","title":"DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis","date":"2024-10-23","arxiv_id":"2410.17564","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_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","sample_list":"/paper/disengcd-a-meta-multigraph-assisted#ran","syntology_url":"https://syntology.ai/paper/2410.17564","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.17564"}},"official":{"repos":["bimk/intelligent-education"],"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"]}}},{"url":"/paper/theoretical-insights-into-line-graph","slug":"theoretical-insights-into-line-graph","title":"Theoretical Insights into Line Graph Transformation on Graph Learning","date":"2024-10-21","arxiv_id":"2410.16138","repositories_listed":1,"syntology":null},{"url":"/paper/learning-graph-quantized-tokenizers-for","slug":"learning-graph-quantized-tokenizers-for","title":"Learning Graph Quantized Tokenizers","date":"2024-10-17","arxiv_id":"2410.13798","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":7,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":3,"phrase":"11 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; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-graph-quantized-tokenizers-for#ran","syntology_url":"https://syntology.ai/paper/2410.13798","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.13798"}},"official":{"repos":["limei0307/GQT"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/cluster-wise-graph-transformer-with-dual","slug":"cluster-wise-graph-transformer-with-dual","title":"Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention","date":"2024-10-09","arxiv_id":"2410.06746","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":10,"n_instrument":3,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":8,"n_pointer_only":14,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 2 honoured, 0 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cluster-wise-graph-transformer-with-dual#ran","syntology_url":"https://syntology.ai/paper/2410.06746","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.06746"}},"official":{"repos":["lumia-group/cluster-wise-graph-transformer"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fedgraph-a-research-library-and-benchmark-for","slug":"fedgraph-a-research-library-and-benchmark-for","title":"FedGraph: A Research Library and Benchmark for Federated Graph Learning","date":"2024-10-08","arxiv_id":"2410.06340","repositories_listed":1,"syntology":null},{"url":"/paper/hyperbrain-anomaly-detection-for-temporal","slug":"hyperbrain-anomaly-detection-for-temporal","title":"HyperBrain: Anomaly Detection for Temporal Hypergraph Brain Networks","date":"2024-10-02","arxiv_id":"2410.02087","repositories_listed":1,"syntology":null},{"url":"/paper/tavrnn-temporal-attention-enhanced","slug":"tavrnn-temporal-attention-enhanced","title":"Graph-Based Representation Learning of Neuronal Dynamics and Behavior","date":"2024-10-01","arxiv_id":"2410.00665","repositories_listed":1,"syntology":null},{"url":"/paper/deep-heterogeneous-contrastive-hyper-graph","slug":"deep-heterogeneous-contrastive-hyper-graph","title":"Deep Heterogeneous Contrastive Hyper-Graph Learning for In-the-Wild Context-Aware Human Activity Recognition","date":"2024-09-27","arxiv_id":"2409.18481","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-redundancy-information-aware","slug":"beyond-redundancy-information-aware","title":"Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learning","date":"2024-09-25","arxiv_id":"2409.17386","repositories_listed":1,"syntology":{"n":10,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":10,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/beyond-redundancy-information-aware#ran","syntology_url":"https://syntology.ai/paper/2409.17386","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.17386"}},"official":{"repos":["zxlearningdeep/infomgf"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/when-witnesses-defend-a-witness-graph","slug":"when-witnesses-defend-a-witness-graph","title":"When Witnesses Defend: A Witness Graph Topological Layer for Adversarial Graph Learning","date":"2024-09-21","arxiv_id":"2409.14161","repositories_listed":1,"syntology":null},{"url":"/paper/online-learning-of-expanding-graphs","slug":"online-learning-of-expanding-graphs","title":"Online Learning Of Expanding Graphs","date":"2024-09-13","arxiv_id":"2409.08660","repositories_listed":1,"syntology":null},{"url":"/paper/cliqueph-higher-order-information-for-graph","slug":"cliqueph-higher-order-information-for-graph","title":"CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs","date":"2024-09-12","arxiv_id":"2409.08217","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cliqueph-higher-order-information-for-graph#ran","syntology_url":"https://syntology.ai/paper/2409.08217","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.08217"}},"official":{"repos":["DavideBuffelli/CliquePH"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-faster-graph-partitioning-via-pre","slug":"towards-faster-graph-partitioning-via-pre","title":"Towards Faster Graph Partitioning via Pre-training and Inductive Inference","date":"2024-09-01","arxiv_id":"2409.00670","repositories_listed":1,"syntology":null},{"url":"/paper/openfgl-a-comprehensive-benchmarks-for","slug":"openfgl-a-comprehensive-benchmarks-for","title":"OpenFGL: A Comprehensive Benchmark for Federated Graph Learning","date":"2024-08-29","arxiv_id":"2408.16288","repositories_listed":1,"syntology":null}],"record_sha256":"5a1ba10c1ae00ba302194f15c1970abcd9b0df3e5f990f9fba33f13205ce832c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}