Loading…
Conformal load prediction with transductive graph autoencoders
Predicting edge weights on graphs has various applications, from transportation systems to social networks. This paper describes a Graph Neural Network (GNN) approach for edge weight prediction with guaranteed coverage. We leverage conformal prediction to calibrate the GNN outputs and produce valid...
Saved in:
| Published in: | Machine learning 2025-03, Vol.114 (3), p.54, Article 54 |
|---|---|
| Main Authors: | , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | |
|---|---|
| cites | cdi_FETCH-LOGICAL-c159w-b958d3d6207cd58bc2caabda00b8c2262b5806f67d9bcca70d39ce320ad53fb53 |
| container_end_page | |
| container_issue | 3 |
| container_start_page | 54 |
| container_title | Machine learning |
| container_volume | 114 |
| creator | Luo, Rui Colombo, Nicolo |
| description | Predicting edge weights on graphs has various applications, from transportation systems to social networks. This paper describes a Graph Neural Network (GNN) approach for edge weight prediction with guaranteed coverage. We leverage conformal prediction to calibrate the GNN outputs and produce valid prediction intervals. We handle data heteroscedasticity through error reweighting and Conformalized Quantile Regression (CQR). We compare the performance of our method against baseline techniques on real-world transportation datasets. Our approach has better coverage and efficiency than all baselines and showcases robustness and adaptability. |
| doi_str_mv | 10.1007/s10994-024-06713-w |
| format | article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3164194182</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3164194182</sourcerecordid><originalsourceid>FETCH-LOGICAL-c159w-b958d3d6207cd58bc2caabda00b8c2262b5806f67d9bcca70d39ce320ad53fb53</originalsourceid><addsrcrecordid>eNp9kE1LxDAQhoMouK7-AU8Fz9FJ0qTpRZDFL1jwoueQr-522W1q0lr890YrePMwDAzP-w48CF0SuCYA1U0iUNclBppHVITh6QgtCK8YBi74MVqAlBwLQvkpOktpBwBUSLFAt6vQNSEe9L7YB-2KPnrX2qENXTG1w7YYou6SG_PlwxebqPttocch-M4G52M6RyeN3id_8buX6O3h_nX1hNcvj8-ruzW2hNcTNjWXjjlBobKOS2Op1do4DWCkpVRQwyWIRlSuNtbqChyrrWcUtOOsMZwt0dXc28fwPvo0qF0YY5dfKkZESeqSSJopOlM2hpSib1Qf24OOn4qA-vakZk8qe1I_ntSUQ2wOpQx3Gx__qv9JfQG6qGy_</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3164194182</pqid></control><display><type>article</type><title>Conformal load prediction with transductive graph autoencoders</title><source>Springer Nature</source><creator>Luo, Rui ; Colombo, Nicolo</creator><creatorcontrib>Luo, Rui ; Colombo, Nicolo</creatorcontrib><description>Predicting edge weights on graphs has various applications, from transportation systems to social networks. This paper describes a Graph Neural Network (GNN) approach for edge weight prediction with guaranteed coverage. We leverage conformal prediction to calibrate the GNN outputs and produce valid prediction intervals. We handle data heteroscedasticity through error reweighting and Conformalized Quantile Regression (CQR). We compare the performance of our method against baseline techniques on real-world transportation datasets. Our approach has better coverage and efficiency than all baselines and showcases robustness and adaptability.</description><identifier>ISSN: 0885-6125</identifier><identifier>EISSN: 1573-0565</identifier><identifier>DOI: 10.1007/s10994-024-06713-w</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Artificial Intelligence ; Computer Science ; Control ; Graph neural networks ; Machine Learning ; Mechatronics ; Natural Language Processing (NLP) ; Predictions ; Robotics ; Simulation and Modeling ; Social networks ; Transportation systems</subject><ispartof>Machine learning, 2025-03, Vol.114 (3), p.54, Article 54</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2025 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>Copyright Springer Nature B.V. Mar 2025</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c159w-b958d3d6207cd58bc2caabda00b8c2262b5806f67d9bcca70d39ce320ad53fb53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Luo, Rui</creatorcontrib><creatorcontrib>Colombo, Nicolo</creatorcontrib><title>Conformal load prediction with transductive graph autoencoders</title><title>Machine learning</title><addtitle>Mach Learn</addtitle><description>Predicting edge weights on graphs has various applications, from transportation systems to social networks. This paper describes a Graph Neural Network (GNN) approach for edge weight prediction with guaranteed coverage. We leverage conformal prediction to calibrate the GNN outputs and produce valid prediction intervals. We handle data heteroscedasticity through error reweighting and Conformalized Quantile Regression (CQR). We compare the performance of our method against baseline techniques on real-world transportation datasets. Our approach has better coverage and efficiency than all baselines and showcases robustness and adaptability.</description><subject>Artificial Intelligence</subject><subject>Computer Science</subject><subject>Control</subject><subject>Graph neural networks</subject><subject>Machine Learning</subject><subject>Mechatronics</subject><subject>Natural Language Processing (NLP)</subject><subject>Predictions</subject><subject>Robotics</subject><subject>Simulation and Modeling</subject><subject>Social networks</subject><subject>Transportation systems</subject><issn>0885-6125</issn><issn>1573-0565</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2025</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LxDAQhoMouK7-AU8Fz9FJ0qTpRZDFL1jwoueQr-522W1q0lr890YrePMwDAzP-w48CF0SuCYA1U0iUNclBppHVITh6QgtCK8YBi74MVqAlBwLQvkpOktpBwBUSLFAt6vQNSEe9L7YB-2KPnrX2qENXTG1w7YYou6SG_PlwxebqPttocch-M4G52M6RyeN3id_8buX6O3h_nX1hNcvj8-ruzW2hNcTNjWXjjlBobKOS2Op1do4DWCkpVRQwyWIRlSuNtbqChyrrWcUtOOsMZwt0dXc28fwPvo0qF0YY5dfKkZESeqSSJopOlM2hpSib1Qf24OOn4qA-vakZk8qe1I_ntSUQ2wOpQx3Gx__qv9JfQG6qGy_</recordid><startdate>20250301</startdate><enddate>20250301</enddate><creator>Luo, Rui</creator><creator>Colombo, Nicolo</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20250301</creationdate><title>Conformal load prediction with transductive graph autoencoders</title><author>Luo, Rui ; Colombo, Nicolo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c159w-b958d3d6207cd58bc2caabda00b8c2262b5806f67d9bcca70d39ce320ad53fb53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2025</creationdate><topic>Artificial Intelligence</topic><topic>Computer Science</topic><topic>Control</topic><topic>Graph neural networks</topic><topic>Machine Learning</topic><topic>Mechatronics</topic><topic>Natural Language Processing (NLP)</topic><topic>Predictions</topic><topic>Robotics</topic><topic>Simulation and Modeling</topic><topic>Social networks</topic><topic>Transportation systems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Luo, Rui</creatorcontrib><creatorcontrib>Colombo, Nicolo</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Machine learning</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Luo, Rui</au><au>Colombo, Nicolo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Conformal load prediction with transductive graph autoencoders</atitle><jtitle>Machine learning</jtitle><stitle>Mach Learn</stitle><date>2025-03-01</date><risdate>2025</risdate><volume>114</volume><issue>3</issue><spage>54</spage><pages>54-</pages><artnum>54</artnum><issn>0885-6125</issn><eissn>1573-0565</eissn><abstract>Predicting edge weights on graphs has various applications, from transportation systems to social networks. This paper describes a Graph Neural Network (GNN) approach for edge weight prediction with guaranteed coverage. We leverage conformal prediction to calibrate the GNN outputs and produce valid prediction intervals. We handle data heteroscedasticity through error reweighting and Conformalized Quantile Regression (CQR). We compare the performance of our method against baseline techniques on real-world transportation datasets. Our approach has better coverage and efficiency than all baselines and showcases robustness and adaptability.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10994-024-06713-w</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0885-6125 |
| ispartof | Machine learning, 2025-03, Vol.114 (3), p.54, Article 54 |
| issn | 0885-6125 1573-0565 |
| language | eng |
| recordid | cdi_proquest_journals_3164194182 |
| source | Springer Nature |
| subjects | Artificial Intelligence Computer Science Control Graph neural networks Machine Learning Mechatronics Natural Language Processing (NLP) Predictions Robotics Simulation and Modeling Social networks Transportation systems |
| title | Conformal load prediction with transductive graph autoencoders |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-22T03%3A51%3A24IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Conformal%20load%20prediction%20with%20transductive%20graph%20autoencoders&rft.jtitle=Machine%20learning&rft.au=Luo,%20Rui&rft.date=2025-03-01&rft.volume=114&rft.issue=3&rft.spage=54&rft.pages=54-&rft.artnum=54&rft.issn=0885-6125&rft.eissn=1573-0565&rft_id=info:doi/10.1007/s10994-024-06713-w&rft_dat=%3Cproquest_cross%3E3164194182%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c159w-b958d3d6207cd58bc2caabda00b8c2262b5806f67d9bcca70d39ce320ad53fb53%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=3164194182&rft_id=info:pmid/&rfr_iscdi=true |