{"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-classification/papers/7","list_of":"/task/graph-classification","task":"Graph Classification","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":7,"pages_in_order":10,"rows_per_page":100,"rows":[601,700],"of":927,"counts":{"archive_papers_tagged":927,"with_a_code_link":483,"where_syntology_ran_a_sample":174,"not_listed_spam_title":0,"listed":927,"listed_where_code_ran":174,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":150,"every_run_a_failure_of_syntologys_instrument":24,"listed_with_a_run_with_no_instrument_failure":150,"listed_every_run_a_failure_of_syntologys_instrument":24,"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-classification","prev":"/task/graph-classification/papers/6","next":"/task/graph-classification/papers/8","papers":[{"url":null,"slug":"rahnet-retrieval-augmented-hybrid-network-for","title":"RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification","date":"2023-08-04","arxiv_id":"2308.02335","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-graph-classification-and-link","title":"A Survey on Graph Classification and Link Prediction based on GNN","date":"2023-07-03","arxiv_id":"2307.00865","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamical-graph-echo-state-networks-with","title":"Dynamical Graph Echo State Networks with Snapshot Merging for Dissemination Process Classification","date":"2023-07-03","arxiv_id":"2307.01237","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-evolution-kernel-method-for-graph","title":"An Evolution Kernel Method for Graph Classification through Heat Diffusion Dynamics","date":"2023-06-26","arxiv_id":"2306.14688","repositories_listed":0,"syntology":null},{"url":null,"slug":"in-process-global-interpretation-for-graph","title":"Globally Interpretable Graph Learning via Distribution Matching","date":"2023-06-18","arxiv_id":"2306.10447","repositories_listed":0,"syntology":null},{"url":null,"slug":"structure-sensitive-graph-dictionary","title":"Structure-Sensitive Graph Dictionary Embedding for Graph Classification","date":"2023-06-18","arxiv_id":"2306.10505","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-and-position-aware-learning-in","title":"Explainable and Position-Aware Learning in Digital Pathology","date":"2023-06-14","arxiv_id":"2306.08198","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-and-graph","title":"Self-supervised Learning and Graph Classification under Heterophily","date":"2023-06-14","arxiv_id":"2306.08469","repositories_listed":0,"syntology":null},{"url":null,"slug":"coco-a-coupled-contrastive-framework-for","title":"CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification","date":"2023-06-08","arxiv_id":"2306.04979","repositories_listed":0,"syntology":null},{"url":null,"slug":"xinsight-revealing-model-insights-for-gnns","title":"XInsight: Revealing Model Insights for GNNs with Flow-based Explanations","date":"2023-06-07","arxiv_id":"2306.04791","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-classification-gaussian-processes-via","title":"Graph Classification Gaussian Processes via Spectral Features","date":"2023-06-06","arxiv_id":"2306.03770","repositories_listed":0,"syntology":null},{"url":null,"slug":"explaining-and-adapting-graph-conditional","title":"Explaining and Adapting Graph Conditional Shift","date":"2023-06-05","arxiv_id":"2306.03256","repositories_listed":0,"syntology":null},{"url":null,"slug":"message-passing-selection-towards","title":"Message-passing selection: Towards interpretable GNNs for graph classification","date":"2023-06-03","arxiv_id":"2306.02081","repositories_listed":0,"syntology":null},{"url":null,"slug":"epic-graph-augmentation-with-edit-path","title":"EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost","date":"2023-06-02","arxiv_id":"2306.01310","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-rewiring-actually-helpful-in-graph-neural","title":"Is Rewiring Actually Helpful in Graph Neural Networks?","date":"2023-05-31","arxiv_id":"2305.19717","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-semi-supervised-universal-graph","title":"Towards Semi-supervised Universal Graph Classification","date":"2023-05-31","arxiv_id":"2305.19598","repositories_listed":0,"syntology":null},{"url":null,"slug":"gimm-infomin-max-for-automated-graph","title":"GIMM: InfoMin-Max for Automated Graph Contrastive Learning","date":"2023-05-27","arxiv_id":"2305.17437","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-the-intrinsic-usefulness-of","title":"Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies","date":"2023-05-25","arxiv_id":"2305.15961","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-ante-hoc-graph-explainer-using-bilevel","title":"Robust Ante-hoc Graph Explainer using Bilevel Optimization","date":"2023-05-25","arxiv_id":"2305.15745","repositories_listed":0,"syntology":null},{"url":null,"slug":"2305-14814","title":"What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding","date":"2023-05-24","arxiv_id":"2305.14814","repositories_listed":0,"syntology":null},{"url":null,"slug":"size-generalizability-of-graph-neural","title":"Size Generalization of Graph Neural Networks on Biological Data: Insights and Practices from the Spectral Perspective","date":"2023-05-24","arxiv_id":"2305.15611","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequential-graph-neural-networks-for-source","title":"Sequential Graph Neural Networks for Source Code Vulnerability Identification","date":"2023-05-23","arxiv_id":"2306.05375","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-graph-reprogramming","title":"Deep Graph Reprogramming","date":"2023-04-28","arxiv_id":"2304.14593","repositories_listed":0,"syntology":null},{"url":null,"slug":"tgnn-a-joint-semi-supervised-framework-for","title":"TGNN: A Joint Semi-supervised Framework for Graph-level Classification","date":"2023-04-23","arxiv_id":"2304.11688","repositories_listed":0,"syntology":null},{"url":null,"slug":"id-mixgcl-identity-mixup-for-graph","title":"ID-MixGCL: Identity Mixup for Graph Contrastive Learning","date":"2023-04-20","arxiv_id":"2304.10045","repositories_listed":0,"syntology":null},{"url":null,"slug":"structural-imbalance-aware-graph-augmentation","title":"Structural Imbalance Aware Graph Augmentation Learning","date":"2023-03-24","arxiv_id":"2303.13757","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-of-graph-neural-networks-for","title":"A Comparison of Graph Neural Networks for Malware Classification","date":"2023-03-22","arxiv_id":"2303.12812","repositories_listed":0,"syntology":null},{"url":null,"slug":"mpool-motif-based-graph-pooling","title":"MPool: Motif-Based Graph Pooling","date":"2023-03-07","arxiv_id":"2303.03654","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-positional-encoding-via-random-feature","title":"Graph Positional Encoding via Random Feature Propagation","date":"2023-03-06","arxiv_id":"2303.02918","repositories_listed":0,"syntology":null},{"url":null,"slug":"aerk-aligned-entropic-reproducing-kernels","title":"AERK: Aligned Entropic Reproducing Kernels through Continuous-time Quantum Walks","date":"2023-03-04","arxiv_id":"2303.03396","repositories_listed":0,"syntology":null},{"url":"/paper/diffusing-graph-attention","slug":"diffusing-graph-attention","title":"Diffusing Graph Attention","date":"2023-03-01","arxiv_id":"2303.00613","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-semantic-backdoor-attack-against-graph","title":"A semantic backdoor attack against Graph Convolutional Networks","date":"2023-02-28","arxiv_id":"2302.14353","repositories_listed":0,"syntology":null},{"url":null,"slug":"sgl-pt-a-strong-graph-learner-with-graph","title":"SGL-PT: A Strong Graph Learner with Graph Prompt Tuning","date":"2023-02-24","arxiv_id":"2302.12449","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-expressivity-of-persistent-homology-in","title":"On the Expressivity of Persistent Homology in Graph Learning","date":"2023-02-20","arxiv_id":"2302.09826","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-graph-generation-to-graph-classification","title":"From Graph Generation to Graph Classification","date":"2023-02-15","arxiv_id":"2302.07989","repositories_listed":0,"syntology":null},{"url":null,"slug":"bi-level-multi-objective-evolutionary","title":"Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search","date":"2023-02-06","arxiv_id":"2302.02565","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-graph-level-clustering-using-pseudo","title":"Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network","date":"2023-02-05","arxiv_id":"2302.02369","repositories_listed":0,"syntology":null},{"url":null,"slug":"structural-explanations-for-graph-neural","title":"Structural Explanations for Graph Neural Networks using HSIC","date":"2023-02-04","arxiv_id":"2302.02139","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-for-graph","title":"Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Data","date":"2023-01-29","arxiv_id":"2301.12361","repositories_listed":0,"syntology":null},{"url":null,"slug":"dbgdgm-dynamic-brain-graph-deep-generative","title":"DBGDGM: Dynamic Brain Graph Deep Generative Model","date":"2023-01-26","arxiv_id":"2301.11408","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-scattering-beyond-wavelet-shackles","title":"Graph Scattering beyond Wavelet Shackles","date":"2023-01-26","arxiv_id":"2301.11456","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-joint-whole-slide","title":"Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer","date":"2023-01-07","arxiv_id":"2301.02933","repositories_listed":0,"syntology":null},{"url":null,"slug":"ganexplainer-gan-based-graph-neural-networks","title":"GANExplainer: GAN-based Graph Neural Networks Explainer","date":"2022-12-30","arxiv_id":"2301.00012","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-learning-for-anomaly-analytics","title":"Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges","date":"2022-12-11","arxiv_id":"2212.05532","repositories_listed":0,"syntology":null},{"url":null,"slug":"qesk-quantum-based-entropic-subtree-kernels","title":"QESK: Quantum-based Entropic Subtree Kernels for Graph Classification","date":"2022-12-10","arxiv_id":"2212.05228","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-graph-neural-networks-and","title":"Application of Graph Neural Networks and graph descriptors for graph classification","date":"2022-11-07","arxiv_id":"2211.03666","repositories_listed":0,"syntology":null},{"url":null,"slug":"haqjsk-hierarchical-aligned-quantum-jensen","title":"HAQJSK: Hierarchical-Aligned Quantum Jensen-Shannon Kernels for Graph Classification","date":"2022-11-05","arxiv_id":"2211.02904","repositories_listed":0,"syntology":null},{"url":null,"slug":"weisfeiler-and-leman-go-hyperbolic-learning","title":"Weisfeiler and Leman go Hyperbolic: Learning Distance Preserving Node Representations","date":"2022-11-04","arxiv_id":"2211.02501","repositories_listed":0,"syntology":null},{"url":null,"slug":"o-gnns-deep-graph-neural-networks-enhanced-by","title":"Improving Graph Neural Networks with Learnable Propagation Operators","date":"2022-10-31","arxiv_id":"2210.17224","repositories_listed":0,"syntology":null},{"url":"/paper/beyond-homophily-with-graph-echo-state-1","slug":"beyond-homophily-with-graph-echo-state-1","title":"Beyond Homophily with Graph Echo State Networks","date":"2022-10-27","arxiv_id":"2210.15731","repositories_listed":0,"syntology":null},{"url":null,"slug":"hcl-improving-graph-representation-with","title":"HCL: Improving Graph Representation with Hierarchical Contrastive Learning","date":"2022-10-21","arxiv_id":"2210.12020","repositories_listed":0,"syntology":null},{"url":null,"slug":"test-time-training-for-graph-neural-networks","title":"Test-Time Training for Graph Neural Networks","date":"2022-10-17","arxiv_id":"2210.08813","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-graph-neural-networks-via-adaptive","title":"Boosting Graph Neural Networks via Adaptive Knowledge Distillation","date":"2022-10-12","arxiv_id":"2210.05920","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-classification-via-discriminative-edge","title":"Graph Classification via Discriminative Edge Feature Learning","date":"2022-10-05","arxiv_id":"2210.02060","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-anomaly-detection-with-graph-neural","title":"Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges","date":"2022-09-29","arxiv_id":"2209.14930","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-scale-graph-signature-for-persistence","title":"A Multi-scale Graph Signature for Persistence Diagrams based on Return Probabilities of Random Walks","date":"2022-09-28","arxiv_id":"2209.14264","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-reconstruction-and-parcellation-of","title":"Joint Reconstruction and Parcellation of Cortical Surfaces","date":"2022-09-19","arxiv_id":"2210.01772","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-cross-view-disentangled-graph","title":"Graph Contrastive Learning with Cross-view Reconstruction","date":"2022-09-16","arxiv_id":"2209.07699","repositories_listed":0,"syntology":null},{"url":null,"slug":"spgp-structure-prototype-guided-graph-pooling","title":"SPGP: Structure Prototype Guided Graph Pooling","date":"2022-09-16","arxiv_id":"2209.07817","repositories_listed":0,"syntology":null},{"url":null,"slug":"defending-against-backdoor-attack-on-graph","title":"Defending Against Backdoor Attack on Graph Nerual Network by Explainability","date":"2022-09-07","arxiv_id":"2209.02902","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforced-continual-learning-for-graphs","title":"Reinforced Continual Learning for Graphs","date":"2022-09-04","arxiv_id":"2209.01556","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-class-aware-representation-refinement","title":"A Class-Aware Representation Refinement Framework for Graph Classification","date":"2022-09-02","arxiv_id":"2209.00936","repositories_listed":0,"syntology":null},{"url":null,"slug":"learnable-filters-for-geometric-scattering","title":"Learnable Filters for Geometric Scattering Modules","date":"2022-08-15","arxiv_id":"2208.07458","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-adversarial-attacks-on-graph","title":"Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classification","date":"2022-08-13","arxiv_id":"2208.06651","repositories_listed":0,"syntology":null},{"url":null,"slug":"more-interpretable-graph-similarity","title":"More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference","date":"2022-08-09","arxiv_id":"2208.04580","repositories_listed":0,"syntology":null},{"url":null,"slug":"grease-generate-factual-and-counterfactual","title":"GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations","date":"2022-08-04","arxiv_id":"2208.04222","repositories_listed":0,"syntology":null},{"url":null,"slug":"maximal-independent-vertex-set-applied-to","title":"Maximal Independent Vertex Set applied to Graph Pooling","date":"2022-08-02","arxiv_id":"2208.01648","repositories_listed":0,"syntology":null},{"url":null,"slug":"label-only-membership-inference-attack","title":"Label-Only Membership Inference Attack against Node-Level Graph Neural Networks","date":"2022-07-27","arxiv_id":"2207.13766","repositories_listed":0,"syntology":null},{"url":null,"slug":"similarity-aware-positive-instance-sampling-1","title":"Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training","date":"2022-06-23","arxiv_id":"2206.11959","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-ood-detection-in-graph-classification","title":"Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective","date":"2022-06-21","arxiv_id":"2206.10691","repositories_listed":0,"syntology":null},{"url":"/paper/0-1-deep-neural-networks-via-block-coordinate","slug":"0-1-deep-neural-networks-via-block-coordinate","title":"0/1 Deep Neural Networks via Block Coordinate Descent","date":"2022-06-19","arxiv_id":"2206.09379","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-hierarchical-graph","title":"Semi-Supervised Hierarchical Graph Classification","date":"2022-06-11","arxiv_id":"2206.05416","repositories_listed":0,"syntology":null},{"url":null,"slug":"we-cannot-guarantee-safety-the-undecidability","title":"Fundamental Limits in Formal Verification of Message-Passing Neural Networks","date":"2022-06-10","arxiv_id":"2206.05070","repositories_listed":0,"syntology":null},{"url":null,"slug":"asynchronous-neural-networks-for-learning-in","title":"Asynchronous Neural Networks for Learning in Graphs","date":"2022-05-24","arxiv_id":"2205.12245","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-order-pooling-for-graph-neural-networks","title":"High-Order Pooling for Graph Neural Networks with Tensor Decomposition","date":"2022-05-24","arxiv_id":"2205.11691","repositories_listed":0,"syntology":null},{"url":null,"slug":"kgnn-harnessing-kernel-based-networks-for","title":"KGNN: Harnessing Kernel-based Networks for Semi-supervised Graph Classification","date":"2022-05-21","arxiv_id":"2205.10550","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-networks-are-more-powerful-than","title":"Representation Power of Graph Neural Networks: Improved Expressivity via Algebraic Analysis","date":"2022-05-19","arxiv_id":"2205.09801","repositories_listed":0,"syntology":null},{"url":null,"slug":"label-invariant-augmentation-for-semi","title":"Label-invariant Augmentation for Semi-Supervised Graph Classification","date":"2022-05-19","arxiv_id":"2205.09802","repositories_listed":0,"syntology":null},{"url":null,"slug":"parallel-and-distributed-graph-neural","title":"Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis","date":"2022-05-19","arxiv_id":"2205.09702","repositories_listed":0,"syntology":null},{"url":null,"slug":"ndggnet-a-node-independent-gate-based-graph","title":"NDGGNET-A Node Independent Gate based Graph Neural Networks","date":"2022-05-11","arxiv_id":"2205.05348","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-temporal-associations-representation","title":"Spatial-temporal associations representation and application for process monitoring using graph convolution neural network","date":"2022-05-11","arxiv_id":"2205.05250","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-free-adversarial-knowledge-distillation","title":"Data-Free Adversarial Knowledge Distillation for Graph Neural Networks","date":"2022-05-08","arxiv_id":"2205.03811","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustered-graph-matching-for-label-recovery","title":"Clustered Graph Matching for Label Recovery and Graph Classification","date":"2022-05-06","arxiv_id":"2205.03486","repositories_listed":0,"syntology":null},{"url":null,"slug":"fastgcl-fast-self-supervised-learning-on","title":"FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation","date":"2022-05-02","arxiv_id":"2205.00905","repositories_listed":0,"syntology":null},{"url":null,"slug":"dotin-dropping-task-irrelevant-nodes-for-gnns","title":"DOTIN: Dropping Task-Irrelevant Nodes for GNNs","date":"2022-04-28","arxiv_id":"2204.13429","repositories_listed":0,"syntology":null},{"url":null,"slug":"liftpool-lifting-based-graph-pooling-for","title":"LiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning","date":"2022-04-27","arxiv_id":"2204.12881","repositories_listed":0,"syntology":null},{"url":null,"slug":"softedge-regularizing-graph-classification","title":"SoftEdge: Regularizing Graph Classification with Random Soft Edges","date":"2022-04-21","arxiv_id":"2204.10390","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-graph-structure-learning-using","title":"Multi-view graph structure learning using subspace merging on Grassmann manifold","date":"2022-04-11","arxiv_id":"2204.05258","repositories_listed":0,"syntology":null},{"url":"/paper/relational-reasoning-over-spatial-temporal","slug":"relational-reasoning-over-spatial-temporal","title":"Relational Reasoning Over Spatial-Temporal Graphs for Video Summarization","date":"2022-04-06","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"graphcoco-graph-complementary-contrastive","title":"On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach","date":"2022-03-24","arxiv_id":"2203.12821","repositories_listed":0,"syntology":null},{"url":null,"slug":"twin-weisfeiler-lehman-high-expressive-gnns","title":"Twin Weisfeiler-Lehman: High Expressive GNNs for Graph Classification","date":"2022-03-22","arxiv_id":"2203.11683","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-contrastive-learning-with","title":"Supervised Contrastive Learning with Structure Inference for Graph Classification","date":"2022-03-15","arxiv_id":"2203.07691","repositories_listed":0,"syntology":null},{"url":null,"slug":"flurry-a-fast-framework-for-reproducible","title":"Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning","date":"2022-03-05","arxiv_id":"2203.02744","repositories_listed":0,"syntology":null},{"url":null,"slug":"distribution-preserving-graph-representation","title":"Distribution Preserving Graph Representation Learning","date":"2022-02-27","arxiv_id":"2202.13428","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-data-augmentations-for-graph","title":"Automated Data Augmentations for Graph Classification","date":"2022-02-26","arxiv_id":"2202.13248","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-deep-learning-for-graphs","title":"Bayesian Deep Learning for Graphs","date":"2022-02-24","arxiv_id":"2202.12348","repositories_listed":0,"syntology":null},{"url":null,"slug":"relation-regularized-scene-graph-generation","title":"Relation Regularized Scene Graph Generation","date":"2022-02-22","arxiv_id":"2202.10826","repositories_listed":0,"syntology":null},{"url":null,"slug":"degree-preserving-randomized-response-for","title":"Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy","date":"2022-02-21","arxiv_id":"2202.10209","repositories_listed":0,"syntology":null},{"url":null,"slug":"graphshap-motif-based-explanations-for-black","title":"GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs","date":"2022-02-17","arxiv_id":"2202.08815","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-amidst-non-iid-graph-data-a","title":"Fairness Amidst Non-IID Graph Data: A Literature Review","date":"2022-02-15","arxiv_id":"2202.07170","repositories_listed":0,"syntology":null}],"record_sha256":"33e7ac665fb8302ee23ab034be8006842c921e0a07cce725751aabed7c13b8a3","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}