{"schema_version":"onlylabs.public_signal.v1","title":"Amazon (Nova) Repo: amazon-science/Automatic-Table-to-Graph-Generation","description":"Amazon (Nova) repo signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/b5a62c24-ce17-4e11-ab0e-bc50c6ee249a","json_url":"https://onlylabs.fyi/signals/b5a62c24-ce17-4e11-ab0e-bc50c6ee249a/signal.json","generated_at":"2026-06-11T03:57:02.279923+00:00","org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/amazon","dossier_json_url":"https://onlylabs.fyi/labs/amazon/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/b5a62c24-ce17-4e11-ab0e-bc50c6ee249a","signal_json":"https://onlylabs.fyi/signals/b5a62c24-ce17-4e11-ab0e-bc50c6ee249a/signal.json","source":"https://github.com/amazon-science/Automatic-Table-to-Graph-Generation","lab_dossier":"https://onlylabs.fyi/labs/amazon","lab_dossier_json":"https://onlylabs.fyi/labs/amazon/dossier.json","analysis":"https://onlylabs.fyi/analysis/amazon","analysis_json":"https://onlylabs.fyi/analysis/amazon/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/amazon/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":null,"topic_signals_json":null,"topic_feed":null,"data_business":null},"answer_pack":{"answer":"Amazon (Nova) published amazon-science/Automatic-Table-to-Graph-Generation (Python). This repository signal exposes tooling, eval, infrastructure, or model-adjacent work before it may appear in a launch post. 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While GML has seen tremendous growth, the crucial step of converting tabular data into meaningful graphs remains largely manual and unstandardized. AutoG leverages Large Language Models (LLMs) to automate this process, producing graphs that rival those created by human experts. Key Features - Automatic graph schema generation without human intervention - LLM-based solution for high-quality graph construction Installation Step 1: Clone..."},"evidence_pages":[{"url":"https://github.com/amazon-science/Automatic-Table-to-Graph-Generation","final_url":"https://github.com/amazon-science/Automatic-Table-to-Graph-Generation","title":"amazon-science/Automatic-Table-to-Graph-Generation repository metadata","http_status":200,"content_type":"application/json","capture_method":"plain","fetched_at":"2026-06-11T03:57:02.279923+00:00","bytes":20190,"raw_path":"f790873bfb06ff7c15a91c8cca8481133c3c6458bae1c5a48aa86305bce12b96.json","content_hash":"9a19517dac3dfc81e13c85bcee5ed12fc1d5528b6b8d9f1ca6d461c3bcdca90c","excerpt_chars":1200,"truncated":true,"excerpt":"amazon-science/Automatic-Table-to-Graph-Generation Language: Python License: Apache-2.0 Stars: 18 Forks: 9 Open issues: 5 Created: 2025-02-24T21:11:35Z Pushed: 2026-04-21T20:54:39Z Default branch: main Fork: no Archived: no README: AutoG: Towards automatic graph construction from tabular data Summary of updates for Oct 8 version * Improved docker configurations * Improved coding styles * Test suites under main/test_action_new.py * Fix bugs raised by @galogm * Test new programs on all 4dbinfer datasets, welcome issues and PRs if you find any new bugs Introduction Overview AutoG is a novel framework that addresses the critical challenge of automatically constructing high-quality graphs from tabular data for graph machine learning (GML) applications. While GML has seen tremendous growth, the crucial step of converting tabular data into meaningful graphs remains largely manual and unstandardized. AutoG leverages Large Language Models (LLMs) to automate this process, producing graphs that rival those created by human experts. Key Features - Automatic graph schema generation without human intervention - LLM-based solution for high-quality graph construction Installation Step 1: Clone..."}],"related_signals":[{"id":"087c32a2-6ad0-4981-9315-11fdd32a0153","url":"https://onlylabs.fyi/signals/087c32a2-6ad0-4981-9315-11fdd32a0153","source_url":"https://github.com/amazon-science/reskill","title":"amazon-science/reskill","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-06-04T02:13:35+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"},{"id":"e5701aed-6cd3-48dd-bfa6-ef839031e2e8","url":"https://onlylabs.fyi/signals/e5701aed-6cd3-48dd-bfa6-ef839031e2e8","source_url":"https://github.com/amazon-science/dualkv-flash-attn-for-rl","title":"amazon-science/dualkv-flash-attn-for-rl","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-05-27T17:38:58+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"},{"id":"8af28f0c-7331-4b08-b517-e18b3555e503","url":"https://onlylabs.fyi/signals/8af28f0c-7331-4b08-b517-e18b3555e503","source_url":"https://github.com/amazon-science/EvoMAS","title":"amazon-science/EvoMAS","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-05-19T19:23:29+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"},{"id":"e3ff8718-7daa-4ebd-a3e6-3d825c538b74","url":"https://onlylabs.fyi/signals/e3ff8718-7daa-4ebd-a3e6-3d825c538b74","source_url":"https://github.com/amazon-science/adaptive-layerwise-perturbation","title":"amazon-science/adaptive-layerwise-perturbation","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-05-14T17:44:17+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"},{"id":"9afcd328-0124-485c-8ace-9c3ad546e316","url":"https://onlylabs.fyi/signals/9afcd328-0124-485c-8ace-9c3ad546e316","source_url":"https://github.com/amazon-science/temporal-reasoning-dataset","title":"amazon-science/temporal-reasoning-dataset","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-05-13T13:07:08+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"},{"id":"e19ce80b-3d6a-4aaf-9b1a-82d1b19ab682","url":"https://onlylabs.fyi/signals/e19ce80b-3d6a-4aaf-9b1a-82d1b19ab682","source_url":"https://github.com/amazon-science/PROF-GRPO","title":"amazon-science/PROF-GRPO","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-05-12T19:43:55+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"}]}