{"id":"https://openalex.org/W7160652832","doi":"https://doi.org/10.48550/arxiv.2605.06576","title":"On the Safety of Graph Representation Learning","display_name":"On the Safety of Graph Representation Learning","publication_year":2026,"publication_date":"2026-05-07","ids":{"openalex":"https://openalex.org/W7160652832","doi":"https://doi.org/10.48550/arxiv.2605.06576"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.06576","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06576","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.06576","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101568362","display_name":"Xiaoguang Guo","orcid":"https://orcid.org/0000-0002-5837-2021"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Xiaoguang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135665710","display_name":"Zehong Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zehong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135723548","display_name":"Ziming Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Ziming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125639199","display_name":"Shawn Spitzel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Spitzel, Shawn","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135659296","display_name":"Soonwoo Kwon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kwon, Soonwoo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135684944","display_name":"Tianyi Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Tianyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135707783","display_name":"Yanfang Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Yanfang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135688641","display_name":"Chuxu Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Chuxu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.0006000000284984708,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.616100013256073},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.459199994802475},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.436599999666214},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4106000065803528},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.3659999966621399},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3050999939441681}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6664000153541565},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.616100013256073},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.459199994802475},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4555000066757202},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.43799999356269836},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.436599999666214},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41609999537467957},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4106000065803528},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3659999966621399},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30869999527931213},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2874000072479248},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C64339825","wikidata":"https://www.wikidata.org/wiki/Q722659","display_name":"Graph property","level":5,"score":0.26579999923706055},{"id":"https://openalex.org/C98183937","wikidata":"https://www.wikidata.org/wiki/Q2112188","display_name":"Program analysis","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.25459998846054077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.06576","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06576","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.06576","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06576","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.8211466670036316}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Graph":[0],"representation":[1,143],"learning":[2],"(GRL)":[3],"has":[4],"evolved":[5],"from":[6],"topology-only":[7,75],"graph":[8,21,48,50,81,87,148],"embeddings":[9],"to":[10,17],"task-specific":[11],"supervised":[12,78],"GNNs,":[13,79],"and":[14,20,33,83,110,114,145,197],"more":[15],"recently":[16],"reusable":[18],"representations":[19],"foundation":[22],"models":[23],"(GFMs).":[24],"However,":[25],"existing":[26],"evaluations":[27],"mainly":[28],"measure":[29],"clean":[30],"transfer,":[31],"adaptation,":[32,186],"task":[34],"coverage.":[35],"It":[36],"remains":[37],"unclear":[38],"whether":[39],"GRL":[40],"methods":[41,158],"stay":[42],"reliable":[43],"when":[44],"deployment":[45,169],"stresses":[46],"affect":[47],"signals,":[49],"contexts,":[51],"label":[52],"support,":[53],"structural":[54],"groups,":[55],"or":[56,187],"predictive":[57],"evidence.":[58],"We":[59],"introduce":[60],"GRL-Safety,":[61],"a":[62,119],"multi-axis":[63],"safety":[64,101,135,165],"evaluation":[65,91,98,195],"benchmark":[66],"for":[67,174],"GRL.":[68],"GRL-Safety":[69],"evaluates":[70],"twelve":[71],"representative":[72],"methods,":[73,77],"spanning":[74],"embedding":[76],"self-supervised":[80],"models,":[82],"GFMs,":[84],"on":[85],"twenty-five":[86],"datasets":[88],"under":[89],"standardized":[90],"conditions":[92],"while":[93],"preserving":[94],"method-native":[95],"adaptation.":[96],"The":[97,193],"covers":[99],"five":[100],"axes:":[102],"corruption":[103],"robustness,":[104,185],"OOD":[105],"generalization,":[106],"class":[107],"imbalance,":[108],"fairness,":[109],"interpretation,":[111],"with":[112],"per-axis":[113],"sub-condition":[115],"reporting":[116],"rather":[117,150,162],"than":[118,151,163],"single":[120],"aggregate":[121],"score.":[122],"Our":[123],"analysis":[124],"yields":[125],"three":[126],"cross-axis":[127],"insights":[128],"that":[129,182],"can":[130],"inspire":[131],"future":[132],"research.":[133],"First,":[134],"behavior":[136],"is":[137],"shaped":[138],"by":[139,152],"the":[140,146,175],"interaction":[141],"between":[142],"design":[144],"stressed":[147],"factor,":[149],"method":[153],"family":[154],"alone.":[155],"Second,":[156],"foundation-era":[157],"show":[159],"axis-specific":[160],"strengths":[161],"broad":[164],"dominance.":[166],"Third,":[167],"several":[168],"regimes":[170],"remain":[171],"difficult":[172],"even":[173],"best":[176],"evaluated":[177],"method,":[178],"revealing":[179],"capability":[180],"gaps":[181],"require":[183],"new":[184],"training":[188],"objectives":[189],"beyond":[190],"model":[191],"selection.":[192],"benchmark,":[194],"protocols,":[196],"code":[198],"are":[199],"available":[200],"at:":[201],"https://github.com/GXG-CS/GRL-Safety.":[202]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-09T00:00:00"}
