{"url":"/task/edge-classification","name":"Edge Classification","slug":"edge-classification","description_markdown":null,"categories":[],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":56,"papers_with_code":27,"benchmarks":0,"benchmark_tables_in_archive":0,"benchmark_tables_shown":0,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":1,"subtasks":0,"parent_tasks":0},"benchmarks":[],"datasets":[{"url":"/dataset/dtgb","name":"DTGB","full_name":"Dynamic Text-attributed Graph Benchmark","num_papers_in_archive":2}],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":27,"of":27,"tagged_in_all":56,"items":[{"url":"/paper/evolvegcn-evolving-graph-convolutional","title":"EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs","date":"2019-02-26","arxiv_id":"1902.10191","repositories_listed":10,"syntology":{"n":11,"n_ran":3,"n_unverified":8,"n_pointer_only":1}},{"url":"/paper/digress-discrete-denoising-diffusion-for","title":"DiGress: Discrete Denoising diffusion for graph generation","date":"2022-09-29","arxiv_id":"2209.14734","repositories_listed":3,"syntology":{"n":25,"n_ran":15,"n_unverified":10,"n_pointer_only":0}},{"url":"/paper/edge-augmented-graph-transformers-global-self","title":"Global Self-Attention as a Replacement for Graph Convolution","date":"2021-08-07","arxiv_id":"2108.03348","repositories_listed":3,"syntology":{"n":4,"n_ran":0,"n_unverified":4,"n_pointer_only":0}},{"url":"/paper/towards-populating-generalizable-engineering","title":"Retrieval Augmented Generation using Engineering Design Knowledge","date":"2023-07-13","arxiv_id":"2307.06985","repositories_listed":2,"syntology":null},{"url":"/paper/graph-neural-network-for-cell-tracking-in","title":"Graph Neural Network for Cell Tracking in Microscopy Videos","date":"2022-02-09","arxiv_id":"2202.04731","repositories_listed":2,"syntology":null},{"url":"/paper/grape-fast-and-scalable-graph-processing-and","title":"GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding","date":"2021-10-12","arxiv_id":"2110.06196","repositories_listed":2,"syntology":null},{"url":"/paper/adaptive-edge-attention-for-graph-matching","title":"Adaptive Edge Attention for Graph Matching with Outliers","date":"2021-08-19","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/learning-and-reasoning-with-the-graph","title":"Learning and Reasoning with the Graph Structure Representation in Robotic Surgery","date":"2020-07-07","arxiv_id":"2007.03357","repositories_listed":2,"syntology":{"n":8,"n_ran":1,"n_unverified":7,"n_pointer_only":8}},{"url":"/paper/lanenet-real-time-lane-detection-networks-for","title":"LaneNet: Real-Time Lane Detection Networks for Autonomous Driving","date":"2018-07-04","arxiv_id":"1807.01726","repositories_listed":2,"syntology":null},{"url":"/paper/scaling-graph-neural-networks-for-particle","title":"Scaling Graph Neural Networks for Particle Track Reconstruction","date":"2025-04-07","arxiv_id":"2504.04670","repositories_listed":1,"syntology":null},{"url":"/paper/a-great-architecture-for-edge-based-graph","title":"A GREAT Architecture for Edge-Based Graph Problems Like TSP","date":"2024-08-29","arxiv_id":"2408.16717","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":1}},{"url":"/paper/edge-classification-on-graphs-new-directions","title":"Edge Classification on Graphs: New Directions in Topological Imbalance","date":"2024-06-17","arxiv_id":"2406.11685","repositories_listed":1,"syntology":null},{"url":"/paper/dtgb-a-comprehensive-benchmark-for-dynamic","title":"DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs","date":"2024-06-17","arxiv_id":"2406.12072","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":1}},{"url":"/paper/multi-modal-uav-detection-classification-and","title":"Multi-Modal UAV Detection, Classification and Tracking Algorithm -- Technical Report for CVPR 2024 UG2 Challenge","date":"2024-05-26","arxiv_id":"2405.16464","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/high-pileup-particle-tracking-with-object","title":"High Pileup Particle Tracking with Object Condensation","date":"2023-12-06","arxiv_id":"2312.03823","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/3dmotformer-graph-transformer-for-online-3d","title":"3DMOTFormer: Graph Transformer for Online 3D Multi-Object Tracking","date":"2023-08-12","arxiv_id":"2308.06635","repositories_listed":1,"syntology":null},{"url":"/paper/edgeformers-graph-empowered-transformers-for","title":"Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks","date":"2023-02-21","arxiv_id":"2302.11050","repositories_listed":1,"syntology":{"n":7,"n_ran":2,"n_unverified":5,"n_pointer_only":0}},{"url":"/paper/a-framework-for-large-scale-synthetic-graph","title":"A Framework for Large Scale Synthetic Graph Dataset Generation","date":"2022-10-04","arxiv_id":"2210.01944","repositories_listed":1,"syntology":null},{"url":"/paper/graph-representation-learning-beyond-node-and","title":"Graph Representation Learning Beyond Node and Homophily","date":"2022-03-03","arxiv_id":"2203.01564","repositories_listed":1,"syntology":null},{"url":"/paper/heat-holistic-edge-attention-transformer-for","title":"HEAT: Holistic Edge Attention Transformer for Structured Reconstruction","date":"2021-11-30","arxiv_id":"2111.15143","repositories_listed":1,"syntology":null},{"url":"/paper/classifying-dyads-for-militarized-conflict","title":"Classifying Dyads for Militarized Conflict Analysis","date":"2021-09-27","arxiv_id":"2109.12860","repositories_listed":1,"syntology":null},{"url":"/paper/stack-sentence-ordering-with-temporal","title":"STaCK: Sentence Ordering with Temporal Commonsense Knowledge","date":"2021-09-06","arxiv_id":"2109.02247","repositories_listed":1,"syntology":null},{"url":"/paper/charged-particle-tracking-via-edge","title":"Charged particle tracking via edge-classifying interaction networks","date":"2021-03-30","arxiv_id":"2103.16701","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_unverified":0,"n_pointer_only":4}},{"url":"/paper/unmixing-convolutional-features-for-crisp","title":"Unmixing Convolutional Features for Crisp Edge Detection","date":"2020-11-19","arxiv_id":"2011.09808","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-edge-classification-optimal","title":"Real-Time Edge Classification: Optimal Offloading under Token Bucket Constraints","date":"2020-10-26","arxiv_id":"2010.13737","repositories_listed":1,"syntology":null},{"url":"/paper/shearlets-as-feature-extractor-for-semantic","title":"Shearlets as Feature Extractor for Semantic Edge Detection: The Model-Based and Data-Driven Realm","date":"2019-11-27","arxiv_id":"1911.12159","repositories_listed":1,"syntology":null},{"url":"/paper/tensor-graph-convolutional-networks-for","title":"Dynamic Graph Convolutional Networks Using the Tensor M-Product","date":"2019-10-16","arxiv_id":"1910.07643","repositories_listed":1,"syntology":null}],"syntology_records":10,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}