{"url":"/task/graph-representation-learning","name":"Graph Representation Learning","slug":"graph-representation-learning","description_markdown":"The goal of **Graph Representation Learning** is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.\r\n\r\n\r\n<span class=\"description-source\">Source: [SIGN: Scalable Inception Graph Neural Networks ](https://arxiv.org/abs/2004.11198)</span>","categories":[{"name":"Graphs","url":"/area/graphs"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":982,"papers_with_code":479,"benchmarks":1,"benchmark_tables_in_archive":1,"benchmark_tables_shown":1,"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":6,"subtasks":1,"parent_tasks":1},"benchmarks":[{"leaderboard":"/sota/graph-representation-learning-on-coma","slug":"graph-representation-learning-on-coma","dataset":"COMA","dataset_url":"/dataset/coma","rows_in_archive":1,"metrics":["Error (mm)"],"first_row_in_archive_order":{"model":"Pi-net-linear","paper_title":"$Π-$nets: Deep Polynomial Neural Networks","paper_url":"/paper/-nets-deep-polynomial-neural-networks","paper_date":"2020-03-08","arxiv_id":"2003.03828","code_links":[{"title":"grigorisg9gr/polynomial_nets","url":"https://github.com/grigorisg9gr/polynomial_nets"},{"title":"kzkadc/poly-nets","url":"https://github.com/kzkadc/poly-nets"}],"syntology":null}}],"datasets":[{"url":"/dataset/reddit","name":"Reddit","full_name":"","num_papers_in_archive":699},{"url":"/dataset/imdb-binary","name":"IMDB-BINARY","full_name":"","num_papers_in_archive":326},{"url":"/dataset/reddit-binary","name":"REDDIT-BINARY","full_name":"","num_papers_in_archive":150},{"url":"/dataset/coma","name":"COMA","full_name":"COMA","num_papers_in_archive":81},{"url":"/dataset/wikigraphs","name":"WikiGraphs","full_name":"","num_papers_in_archive":4},{"url":"/dataset/myket-android-application-install","name":"Myket Android Application Install","full_name":"","num_papers_in_archive":1}],"subtasks":[{"url":"/task/knowledge-graph-embedding","name":"Knowledge Graph Embedding"}],"parent_tasks":[{"url":"/task/representation-learning","name":"Representation Learning"}],"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":30,"of":479,"tagged_in_all":982,"items":[{"url":"/paper/how-powerful-are-graph-neural-networks","title":"How Powerful are Graph Neural Networks?","date":"2018-10-01","arxiv_id":"1810.00826","repositories_listed":19,"syntology":{"n":10,"n_ran":3,"n_unverified":7,"n_pointer_only":5}},{"url":"/paper/hierarchical-graph-representation-learning","title":"Hierarchical Graph Representation Learning with Differentiable Pooling","date":"2018-06-22","arxiv_id":"1806.08804","repositories_listed":14,"syntology":{"n":20,"n_ran":1,"n_unverified":19,"n_pointer_only":0}},{"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/graphsaint-graph-sampling-based-inductive","title":"GraphSAINT: Graph Sampling Based Inductive Learning Method","date":"2019-07-10","arxiv_id":"1907.04931","repositories_listed":8,"syntology":null},{"url":"/paper/qa-gnn-reasoning-with-language-models-and","title":"QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering","date":"2021-04-13","arxiv_id":"2104.06378","repositories_listed":6,"syntology":{"n":25,"n_ran":1,"n_unverified":24,"n_pointer_only":4}},{"url":"/paper/fast-graph-representation-learning-with","title":"Fast Graph Representation Learning with PyTorch Geometric","date":"2019-03-06","arxiv_id":"1903.02428","repositories_listed":6,"syntology":{"n":2,"n_ran":0,"n_unverified":2,"n_pointer_only":0}},{"url":"/paper/effect-of-choosing-loss-function-when-using-t","title":"Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic Networks","date":"2023-08-13","arxiv_id":"2308.06862","repositories_listed":5,"syntology":null},{"url":"/paper/do-transformers-really-perform-bad-for-graph","title":"Do Transformers Really Perform Bad for Graph Representation?","date":"2021-06-09","arxiv_id":"2106.05234","repositories_listed":5,"syntology":null},{"url":"/paper/sign-scalable-inception-graph-neural-networks","title":"SIGN: Scalable Inception Graph Neural Networks","date":"2020-04-23","arxiv_id":"2004.11198","repositories_listed":5,"syntology":{"n":7,"n_ran":2,"n_unverified":5,"n_pointer_only":0}},{"url":"/paper/a-fair-comparison-of-graph-neural-networks-1","title":"A Fair Comparison of Graph Neural Networks for Graph Classification","date":"2019-12-20","arxiv_id":"1912.09893","repositories_listed":5,"syntology":null},{"url":"/paper/graphgan-graph-representation-learning-with","title":"GraphGAN: Graph Representation Learning with Generative Adversarial Nets","date":"2017-11-22","arxiv_id":"1711.08267","repositories_listed":5,"syntology":null},{"url":"/paper/recipe-for-a-general-powerful-scalable-graph","title":"Recipe for a General, Powerful, Scalable Graph Transformer","date":"2022-05-25","arxiv_id":"2205.12454","repositories_listed":4,"syntology":{"n":21,"n_ran":3,"n_unverified":18,"n_pointer_only":0}},{"url":"/paper/bootstrapped-representation-learning-on","title":"Large-Scale Representation Learning on Graphs via Bootstrapping","date":"2021-02-12","arxiv_id":"2102.06514","repositories_listed":4,"syntology":{"n":8,"n_ran":5,"n_unverified":3,"n_pointer_only":7}},{"url":"/paper/gcc-graph-contrastive-coding-for-graph-neural","title":"GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training","date":"2020-06-17","arxiv_id":"2006.09963","repositories_listed":4,"syntology":{"n":2,"n_ran":0,"n_unverified":2,"n_pointer_only":0}},{"url":"/paper/understanding-negative-sampling-in-graph","title":"Understanding Negative Sampling in Graph Representation Learning","date":"2020-05-20","arxiv_id":"2005.09863","repositories_listed":4,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":1}},{"url":"/paper/transitivity-preserving-graph-representation","title":"Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity","date":"2023-08-18","arxiv_id":"2308.09517","repositories_listed":3,"syntology":{"n":26,"n_ran":5,"n_unverified":21,"n_pointer_only":0}},{"url":"/paper/explanations-as-features-llm-based-features","title":"Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning","date":"2023-05-31","arxiv_id":"2305.19523","repositories_listed":3,"syntology":{"n":7,"n_ran":4,"n_unverified":3,"n_pointer_only":1}},{"url":"/paper/tractable-probabilistic-graph-representation","title":"Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks","date":"2023-05-17","arxiv_id":"2305.10544","repositories_listed":3,"syntology":null},{"url":"/paper/a-critical-look-at-the-evaluation-of-gnns","title":"A critical look at the evaluation of GNNs under heterophily: Are we really making progress?","date":"2023-02-22","arxiv_id":"2302.11640","repositories_listed":3,"syntology":null},{"url":"/paper/simplifying-subgraph-representation-learning","title":"Simplifying Subgraph Representation Learning for Scalable Link Prediction","date":"2023-01-29","arxiv_id":"2301.12562","repositories_listed":3,"syntology":null},{"url":"/paper/a-generalization-of-vit-mlp-mixer-to-graphs","title":"A Generalization of ViT/MLP-Mixer to Graphs","date":"2022-12-27","arxiv_id":"2212.13350","repositories_listed":3,"syntology":{"n":3,"n_ran":2,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/algorithm-and-system-co-design-for-efficient","title":"Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning","date":"2022-02-28","arxiv_id":"2202.13538","repositories_listed":3,"syntology":{"n":4,"n_ran":0,"n_unverified":4,"n_pointer_only":0}},{"url":"/paper/a-survey-of-pretraining-on-graphs-taxonomy","title":"A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications","date":"2022-02-16","arxiv_id":"2202.07893","repositories_listed":3,"syntology":null},{"url":"/paper/structure-aware-transformer-for-graph","title":"Structure-Aware Transformer for Graph Representation Learning","date":"2022-02-07","arxiv_id":"2202.03036","repositories_listed":3,"syntology":{"n":9,"n_ran":4,"n_unverified":5,"n_pointer_only":0}},{"url":"/paper/e-graphsage-a-graph-neural-network-based","title":"E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT","date":"2021-03-30","arxiv_id":"2103.16329","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":1}},{"url":"/paper/towards-a-unified-framework-for-fair-and","title":"Towards a Unified Framework for Fair and Stable Graph Representation Learning","date":"2021-02-25","arxiv_id":"2102.13186","repositories_listed":3,"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/sub-graph-contrast-for-scalable-self","title":"Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning","date":"2020-09-22","arxiv_id":"2009.10273","repositories_listed":3,"syntology":null},{"url":"/paper/towards-deeper-graph-neural-networks","title":"Towards Deeper Graph Neural Networks","date":"2020-07-18","arxiv_id":"2007.09296","repositories_listed":3,"syntology":{"n":3,"n_ran":3,"n_unverified":0,"n_pointer_only":3}},{"url":"/paper/deep-graph-contrastive-representation","title":"Deep Graph Contrastive Representation Learning","date":"2020-06-07","arxiv_id":"2006.04131","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":1}},{"url":"/paper/a-gentle-introduction-to-deep-learning-for","title":"A Gentle Introduction to Deep Learning for Graphs","date":"2019-12-29","arxiv_id":"1912.12693","repositories_listed":3,"syntology":null}],"syntology_records":19,"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"}}