{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T21:33:39Z","timestamp":1791322419311,"version":"4.3.1"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T00:00:00Z","timestamp":1779753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["OAC-2115094,"],"award-info":[{"award-number":["OAC-2115094,"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,5,26]]},"DOI":"10.1145\/3786335.3813177","type":"proceedings-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T03:16:22Z","timestamp":1779419782000},"page":"800-811","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["MoltGraph: A Longitudinal Temporal Graph Dataset of Moltbook for Coordinated-Agent Detection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9693-6520","authenticated-orcid":false,"given":"Kunal","family":"Mukherjee","sequence":"first","affiliation":[{"name":"Virginia Polytechnic Institute and State University, Blacksburg, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2882-6950","authenticated-orcid":false,"given":"Cuneyt","family":"Akcora","sequence":"additional","affiliation":[{"name":"University of Central Florida, Orlando, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9795-9063","authenticated-orcid":false,"given":"Murat","family":"Kantarcioglu","sequence":"additional","affiliation":[{"name":"Virginia Polytechnic Institute and State University, Blacksburg, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,5,26]]},"reference":[{"key":"e_1_3_3_2_2_2","unstructured":"Phoenix\u00a0CS Andrews. 2021. Social Media Futures: What Is Brigading? Tony Blair Institute for Global Change. https:\/\/institute.global\/insights\/tech-and-digitalisation\/social-media-futures-what-brigading"},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"publisher","unstructured":"Albert-L\u00e1szl\u00f3 Barab\u00e1si. 2005. The origin of bursts and heavy tails in human dynamics. Nature 435 7039 (2005) 207\u2013211. 10.1038\/nature03459","DOI":"10.1038\/nature03459"},{"key":"e_1_3_3_2_4_2","volume-title":"Industrialized Disinformation: 2020 Global Inventory of Organised Social Media Manipulation","author":"Bradshaw Samantha","year":"2021","unstructured":"Samantha Bradshaw, Hannah Bailey, and Philip\u00a0N. Howard. 2021. Industrialized Disinformation: 2020 Global Inventory of Organised Social Media Manipulation. Technical Report Working Paper 2021.1. Project on Computational Propaganda, Oxford Internet Institute, University of Oxford. https:\/\/demtech.oii.ox.ac.uk\/wp-content\/uploads\/sites\/12\/2021\/01\/CyberTroop-Report-2020-v.2.pdf"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"publisher","unstructured":"Aaron Clauset Cosma\u00a0Rohilla Shalizi and M.\u00a0E.\u00a0J. Newman. 2009. Power-law distributions in empirical data. SIAM Rev. 51 4 (2009) 661\u2013703. 10.1137\/070710111arXiv:https:\/\/arXiv.org\/abs\/0706.1062.","DOI":"10.1137\/070710111"},{"key":"e_1_3_3_2_6_2","unstructured":"Shangbin Feng Zhaoxuan Tan Herun Wan Ningnan Wang Zilong Chen Binchi Zhang Qinghua Zheng Wenqian Zhang Zhenyu Lei Shujie Yang et\u00a0al. 2022. TwiBot-22: Towards Graph-Based Twitter Bot Detection. arXiv preprint (2022). arxiv:https:\/\/arXiv.org\/abs\/2206.04564\u00a0[cs.SI]"},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"crossref","unstructured":"Shangbin Feng Herun Wan Ningnan Wang and Jundong Li. 2021. BotRGCN: Twitter Bot Detection with Relational Graph Convolutional Networks. arXiv preprint (2021). arxiv:https:\/\/arXiv.org\/abs\/2106.13092\u00a0[cs.SI]","DOI":"10.1145\/3487351.3488336"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"publisher","unstructured":"Emilio Ferrara Onur Varol Clayton Davis Filippo Menczer and Alessandro Flammini. 2016. The Rise of Social Bots. Commun. ACM 59 7 (2016) 96\u2013104. 10.1145\/28187172818717\"\/>","DOI":"10.1145\/2818717"},{"key":"e_1_3_3_2_9_2","unstructured":"Yukun Jiang Yage Zhang Xinyue Shen Michael Backes and Yang Zhang. 2026. \" Humans welcome to observe\": A First Look at the Agent Social Network Moltbook. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2602.10127 (2026)."},{"key":"e_1_3_3_2_10_2","unstructured":"Meta Newsroom. 2021. July 2021 Coordinated Inauthentic Behavior Report. https:\/\/about.fb.com\/news\/2021\/08\/july-2021-coordinated-inauthentic-behavior-report\/"},{"key":"e_1_3_3_2_11_2","unstructured":"Moltbook. 2026. Moltbook: the front page of the agent internet. Website. https:\/\/www.moltbook.com\/ Accessed: 2026-02-24."},{"key":"e_1_3_3_2_12_2","unstructured":"Kunal Mukherjee. 2026. GeoGuard: UWB Timing-Encoded Key Reconstruction for Location-Dependent Geographically Bounded Decryption. arxiv:https:\/\/arXiv.org\/abs\/2511.14032\u00a0[cs.CR] https:\/\/arxiv.org\/abs\/2511.14032"},{"key":"e_1_3_3_2_13_2","unstructured":"Kunal Mukherjee. 2026. Red-Teaming Claude Opus and ChatGPT-based Security Advisors for Trusted Execution Environments. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2602.19450 (2026)."},{"key":"e_1_3_3_2_14_2","unstructured":"Kunal Mukherjee Zulfikar Alom Tran Gia\u00a0Bao Ngo Cuneyt\u00a0Gurcan Akcora and Murat Kantarcioglu. 2026. Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection. arXiv preprint \/ manuscript. Under submission; preprint available."},{"key":"e_1_3_3_2_15_2","volume-title":"KDD Workshop on Machine Learning on Graphs in the Era of Generative AI (MLoG-GenAI)","author":"Mukherjee Kunal","year":"2025","unstructured":"Kunal Mukherjee, Zachary Harrison, and Saeid Balaneshin. 2025. Z-REx: Human-Interpretable GNN Explanations for Real Estate Recommendations. In KDD Workshop on Machine Learning on Graphs in the Era of Generative AI (MLoG-GenAI). Toronto, Canada. Oral presentation."},{"key":"e_1_3_3_2_16_2","unstructured":"Kunal Mukherjee and Murat Kantarcioglu. 2025. LLM-driven Provenance Forensics for Threat Intelligence and Detection. arXiv preprint \/ manuscript. Under submission; preprint available."},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-54776-8_10"},{"key":"e_1_3_3_2_18_2","unstructured":"Kunal Mukherjee Joshua Wiedemeier Tianhao Wang Muhyun Kim Feng Chen Murat Kantarcioglu and Kangkook Jee. 2023. Interpreting gnn-based ids detections using provenance graph structural features. (2023)."},{"key":"e_1_3_3_2_19_2","volume-title":"USENIX Security Symposium (SEC)","author":"Mukherjee Kunal","year":"2023","unstructured":"Kunal Mukherjee, Josh Wiedemeier, Tianhao Wang, James Wei, Feng Chen, Muhyun Kim, Murat Kantarcioglu, and Kangkook Jee. 2023. Evading Provenance-Based ML Detectors with Adversarial System Actions. In USENIX Security Symposium (SEC)."},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-95767-3_8"},{"key":"e_1_3_3_2_21_2","unstructured":"Diogo Pacheco Pik-Mai Hui Christopher Torres-Lugo Bao\u00a0Tran Truong Alessandro Flammini and Filippo Menczer. 2020. Uncovering Coordinated Networks on Social Media: Methods and Case Studies. arXiv preprint (2020). arxiv:https:\/\/arXiv.org\/abs\/2001.05658\u00a0[cs.SI]"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i13.33575"},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"publisher","unstructured":"Kate Starbird. 2019. Disinformation\u2019s Spread: Bots Trolls and All of Us. Nature 571 7766 (2019) 449. 10.1038\/d41586-019-02235-x","DOI":"10.1038\/d41586-019-02235-x"},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v11i1.14871"},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26283"}],"event":{"name":"CAIS '26: ACM Conference on AI and Agentic Systems","location":"San Jose CA USA","acronym":"CAIS '26"},"container-title":["Proceedings of the ACM Conference on AI and Agentic Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3786335.3813177","content-type":"text\/html","content-version":"vor","intended-application":"syndication"}],"deposited":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T03:29:52Z","timestamp":1779420592000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3786335.3813177"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,26]]},"references-count":24,"alternative-id":["10.1145\/3786335.3813177","10.1145\/3786335"],"URL":"https:\/\/doi.org\/10.1145\/3786335.3813177","relation":{},"subject":[],"published":{"date-parts":[[2026,5,26]]},"assertion":[{"value":"2026-05-26","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}