{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T05:10:53Z","timestamp":1760677853643,"version":"build-2065373602"},"reference-count":25,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,15]]},"DOI":"10.1109\/hpec67600.2025.11196094","type":"proceedings-article","created":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T17:35:37Z","timestamp":1760636137000},"page":"1-7","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating Efficiency and Novelty of LLM-Generated Code for Graph Analysis"],"prefix":"10.1109","author":[{"given":"Atieh Barati","family":"Nia","sequence":"first","affiliation":[{"name":"New Jersey Institute of Technology,Department of Data Science,Newark,NJ,USA"}]},{"given":"Mohammad","family":"Dindoost","sequence":"additional","affiliation":[{"name":"New Jersey Institute of Technology,Department of Data Science,Newark,NJ,USA"}]},{"given":"David A.","family":"Bader","sequence":"additional","affiliation":[{"name":"New Jersey Institute of Technology,Department of Data Science,Newark,NJ,USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3641289"},{"article-title":"CodeT: Code generation with generated tests","year":"2022","author":"Chen","key":"ref2"},{"key":"ref3","first-page":"21 558","article-title":"Is your code generated by ChatGPT really correct? rigorous evaluation of large language models for code generation","volume":"36","author":"Liu","year":"2023","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.emnlp-main.1118"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3524842.3528470"},{"key":"ref6","first-page":"41 386","article-title":"Large language models of code fail at completing code with potential bugs","volume":"36","author":"Dinh","year":"2023","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-025-10614-4"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/MSR59073.2023.00082"},{"article-title":"Evaluating large language models trained on code","year":"2021","author":"Chen","key":"ref9"},{"article-title":"Program synthesis with large language models","year":"2021","author":"Austin","key":"ref10"},{"key":"ref11","first-page":"11 506","article-title":"EffiBench: Benchmarking the efficiency of automatically generated code","volume":"37","author":"Huang","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"article-title":"How efficient is LLM-generated code? a rigorous & high-standard benchmark","year":"2024","author":"Qiu","key":"ref12"},{"article-title":"Comparing human and LLM generated code: The jury is still out!","year":"2025","author":"Licorish","key":"ref13"},{"article-title":"Mercury: A code efficiency benchmark for code large language models","year":"2024","author":"Du","key":"ref14"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3650105.3652295"},{"article-title":"Evaluating language models for efficient code generation","year":"2024","author":"Liu","key":"ref16"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/SCAM63643.2024.00020"},{"article-title":"Evaluating the code quality of AI-assisted code generation tools: An empirical study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT","year":"2023","author":"Yeti\u015ftiren","key":"ref18"},{"article-title":"LLM4EFFI: Leveraging large language models to enhance code efficiency and correctness","year":"2025","author":"Ye","key":"ref19"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.emnlp-main.859"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/HPEC58863.2023.10363539"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/11427186_54"},{"key":"ref23","article-title":"Algorithmic aspects of triangle-based network analysis","volume-title":"PhD dissertation","author":"Schank","year":"2007"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/HPEC58863.2023.10363465"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972740.43"}],"event":{"name":"2025 IEEE High Performance Extreme Computing Conference (HPEC)","start":{"date-parts":[[2025,9,15]]},"location":"Wakefield, MA, USA","end":{"date-parts":[[2025,9,19]]}},"container-title":["2025 IEEE High Performance Extreme Computing Conference (HPEC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11196085\/11196088\/11196094.pdf?arnumber=11196094","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T04:42:58Z","timestamp":1760676178000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11196094\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,15]]},"references-count":25,"URL":"https:\/\/doi.org\/10.1109\/hpec67600.2025.11196094","relation":{},"subject":[],"published":{"date-parts":[[2025,9,15]]}}}