{"id":28236376,"url":"https://github.com/passionlab/graphlearning","last_synced_at":"2026-01-30T05:31:21.197Z","repository":{"id":43373116,"uuid":"330018890","full_name":"PASSIONLab/GraphLearning","owner":"PASSIONLab","description":null,"archived":false,"fork":false,"pushed_at":"2022-03-31T20:13:25.000Z","size":56,"stargazers_count":0,"open_issues_count":0,"forks_count":2,"subscribers_count":13,"default_branch":"main","last_synced_at":"2025-05-19T00:14:25.180Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/PASSIONLab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-01-15T20:47:26.000Z","updated_at":"2021-10-28T05:30:37.000Z","dependencies_parsed_at":"2022-08-29T19:21:36.797Z","dependency_job_id":null,"html_url":"https://github.com/PASSIONLab/GraphLearning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PASSIONLab%2FGraphLearning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PASSIONLab%2FGraphLearning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PASSIONLab%2FGraphLearning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PASSIONLab%2FGraphLearning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/PASSIONLab","download_url":"https://codeload.github.com/PASSIONLab/GraphLearning/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PASSIONLab%2FGraphLearning/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":258354028,"owners_count":22687535,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-05-19T00:14:25.042Z","updated_at":"2026-01-30T05:31:21.164Z","avatar_url":"https://github.com/PASSIONLab.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Graph Learning Meetings\n\n# 4/15/2022, lead: Chunxing Yin (Georgia Tech) \n\n## Agenda\n\n- TT compression for DLRM https://proceedings.mlsys.org/paper/2021/hash/979d472a84804b9f647bc185a877a8b5-Abstract.html\n- https://csc.mpi-magdeburg.mpg.de/mpcsc/benner/publications_fiona/talks/2021/Benner-IPAM-2021.pdf \n- https://arxiv.org/pdf/2011.06532.pdf\n\n# 4/1/2022, lead: Alok \n\n## Agenda\n\n- High-performance graph sampling for GNN training\n- https://arxiv.org/pdf/2009.06693.pdf\n- https://arxiv.org/pdf/2203.10983.pdf\n\n\n# 3/4/2022, lead: Yu-Hang\n\n## Agenda\n\n- Model compression for neural networks: [Tensorizing Neural Networks](https://proceedings.neurips.cc/paper/2015/hash/6855456e2fe46a9d49d3d3af4f57443d-Abstract.html).\n- Applications in DLRM, language models (?), and edge computing\n- Implications for parallelism as this increases depth of NN\n- Tensor-train times dense matrix multiplication as a computational primitive?\n\n\n# 2/18/2022, lead: Vivek and Aydin\n\n## Agenda\n\n- SIGN (Scalable Inception Graph Neural Networks)\n- https://arxiv.org/abs/2004.11198\n\n- OGB large-scale challenge\n- https://arxiv.org/abs/2103.09430\n- https://ogb.stanford.edu/kddcup2021/\n\n\n# 2/4/2022, lead: all\n\n## Agenda\n\n- Organizational meeting\n\n\n# 12/2/2021, lead: Andrew\n\n## Agenda\n\n- Hyperbolic embeddings\n- https://arxiv.org/pdf/1804.03329.pdf\n\n- And here's a good introduction to the paper from the authors\nhttps://dawn.cs.stanford.edu/2018/03/19/hyperbolics/\n\n- You should only need to understand the Poincaré disk, which the above resources cover. But, if you want some more background on hyperbolic geometry, this book chapter is good:\nhttp://library.msri.org/books/Book31/files/cannon.pdf\n\n- For those curious to know more about hyperbolic neural networks, here are some more papers on this topic\n\n- Hyperbolic NNs:\nhttps://arxiv.org/abs/1805.09112\n\n- Fully hyperbolic NNs i.e. no tangent space projection:\nhttps://arxiv.org/abs/2105.14686\n\n- Hyperbolic attention networks. I haven't read this yet, but it would be remiss of me not at least mention something about attention\nhttps://arxiv.org/abs/1805.09786\n\n\n# 10/29/2021, lead: Yu-Hang.\n\n## Agenda\n\n- Equivariant GNNs:\n- https://arxiv.org/abs/2102.09844\n\n\n# 8/20/2021, lead: Nick S.\n\n## Agenda\n\n- Alphafold2 or Baker Lab variant:\n- https://www.nature.com/articles/s41586-021-03819-2\n- https://science.sciencemag.org/content/early/2021/07/14/science.abj8754.full\n\n\n# 8/6/2021, lead: Koby\n\n## Agenda\n\n- Graphs, simplicial complexes, and hypergraphs for data modeling: https://arxiv.org/abs/2006.02870\n- and hypergraph learning: https://vision.cornell.edu/se3/wp-content/uploads/2014/09/icml06.pdf\n\n\n# 7/16/2021, lead: Aydin\n\n## Agenda\n\n- A General Graph Neural Network Framework for Link Prediction: https://arxiv.org/pdf/2106.06935.pdf\n- Path Problems in Networks: https://user.eng.umd.edu/~baras/publications/Books/S00245ED1V01Y201001CNT003.pdf\n\n\n# 7/9/2021, lead: Aditi and Nick\n\n## Agenda\n\n- Ensemble learning\n\n\n# 5/7/2021, lead: Yu-Hang\n\n## Agenda\n\n- Graph Neural Tangent Kernels: https://openreview.net/pdf/dd6097df468d83341c8f74f3a83470866d994965.pdf\n\n\n# 4/30/2021, lead: Aydin\n\n## Agenda\n\n- Weisfeiler-Leman Heuristic and associated Graph Kernels: https://www.jmlr.org/papers/volume12/shervashidze11a/shervashidze11a.pdf\n\n\n# 4/16/2021, lead: Prashant\n\n## Agenda\n \n- Graph Attention Networks: https://arxiv.org/abs/1710.10903\n\n# 3/12/2021, lead: Aditi\n\n## Agenda\n \n- Topological Graph Neural Networks: https://arxiv.org/pdf/2102.07835.pdf\n\n\n# 2/26/2021, lead: Nick B.\n\n## Agenda\n \n- MSA Transformer: https://www.biorxiv.org/content/10.1101/2021.02.12.430858v1.full.pdf\n\n# 2/12/2021, lead: Nick S.\n\n## Agenda\n \n- Learning from Protein Structure with Geometric Vector Perceptrons: https://openreview.net/forum?id=1YLJDvSx6J4\n\n\n# 1/29/2021, lead: none\n\n## Agenda \n\n- How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks https://openreview.net/forum?id=UH-cmocLJC\n\n## Minutes\n\n- Solving a series of successively harder DP problems with GNNs.\n  * Needleman-Wunsch on pairs of reads\n  * Smith-Waterman on pairs of reads\n  * Sequence to graph alignment (potentially useful for pangenomes)\n  * Many-to-many sequence alignment\n  * Assembly on error-free reads\n  * Assembly on erroneous reads\n\n\n# 1/15/2021, lead: none\n## Agenda \n\n- https://www.reddit.com/r/MachineLearning/comments/kqazpd/d_why_im_lukewarm_on_graph_neural_networks/\n- https://towardsdatascience.com/predictions-and-hopes-for-graph-ml-in-2021-6af2121c3e3d\n- Combining Label Propagation and Simple Models out-performs Graph Neural Networks: https://openreview.net/forum?id=8E1-f3VhX1o\n\n## Minutes\n\n- Discussion on GNNs vs CNNs, Transformers vs GNNs, and whether we need any induction bias. \n- Discussion on whether the test cases in the Correct\u0026Smooth paper are too simple. \n- Discussion on whether the proposed C\u0026S model is any easier to tune and/or run compared to GNNs. \n- We also talked about the issues with the authors' understanding of the topic in the Reddit post\n- Paper for potential future reading: https://arxiv.org/pdf/1806.01261.pdf\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpassionlab%2Fgraphlearning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpassionlab%2Fgraphlearning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpassionlab%2Fgraphlearning/lists"}