{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:36:20Z","timestamp":1763202980288,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":19,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,12,19]]},"DOI":"10.1145\/3704522.3704537","type":"proceedings-article","created":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T12:29:51Z","timestamp":1735907391000},"page":"41-47","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Graph Representation Learning with WalkLM for Effective Community Detection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6073-1377","authenticated-orcid":false,"given":"Hyun","family":"Lee","sequence":"first","affiliation":[{"name":"Trinity College, Hartford, Connecticut, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2393-6296","authenticated-orcid":false,"given":"Maminur","family":"Islam","sequence":"additional","affiliation":[{"name":"Trinity College, Hartford, Connecticut, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8655-4157","authenticated-orcid":false,"given":"Chris","family":"Yi","sequence":"additional","affiliation":[{"name":"Trinity College, Hartford, Connecticut, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4142-6089","authenticated-orcid":false,"given":"Chandranil (Nil)","family":"Chakraborttii","sequence":"additional","affiliation":[{"name":"Trinity College, Hartford, Connecticut, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,1,3]]},"reference":[{"key":"e_1_3_3_2_2_2","unstructured":"Leman Akoglu Hanghang Tong and Danai Koutra. 2014. Graph-based Anomaly Detection and Description: A Survey. arxiv:https:\/\/arXiv.org\/abs\/1404.4679\u00a0[cs.SI] https:\/\/arxiv.org\/abs\/1404.4679"},{"key":"e_1_3_3_2_3_2","unstructured":"Justin Gilmer Samuel\u00a0S. Schoenholz Patrick\u00a0F. Riley Oriol Vinyals and George\u00a0E. Dahl. 2017. Neural Message Passing for Quantum Chemistry. arxiv:https:\/\/arXiv.org\/abs\/1704.01212\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/1704.01212"},{"key":"e_1_3_3_2_4_2","unstructured":"Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable Feature Learning for Networks. arxiv:https:\/\/arXiv.org\/abs\/1607.00653\u00a0[cs.SI] https:\/\/arxiv.org\/abs\/1607.00653"},{"key":"e_1_3_3_2_5_2","unstructured":"Jiayan Guo Lun Du Hengyu Liu Mengyu Zhou Xinyi He and Shi Han. 2023. GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking. arxiv:https:\/\/arXiv.org\/abs\/2305.15066\u00a0[cs.AI] https:\/\/arxiv.org\/abs\/2305.15066"},{"key":"e_1_3_3_2_6_2","unstructured":"Thomas\u00a0N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. arxiv:https:\/\/arXiv.org\/abs\/1609.02907\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/1609.02907"},{"key":"e_1_3_3_2_7_2","unstructured":"Qimai Li Zhichao Han and Xiao-Ming Wu. 2018. Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning. arxiv:https:\/\/arXiv.org\/abs\/1801.07606\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/1801.07606"},{"key":"e_1_3_3_2_8_2","unstructured":"Yuhan Li Zhixun Li Peisong Wang Jia Li Xiangguo Sun Hong Cheng and Jeffrey\u00a0Xu Yu. 2024. A Survey of Graph Meets Large Language Model: Progress and Future Directions. arxiv:https:\/\/arXiv.org\/abs\/2311.12399\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2311.12399"},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"publisher","unstructured":"Yixin Liu Ming Jin Shirui Pan Chuan Zhou Yu Zheng Feng Xia and Philip Yu. 2022. Graph Self-Supervised Learning: A Survey. IEEE Transactions on Knowledge and Data Engineering (2022) 1\u20131. 10.1109\/tkde.2022.3172903 https:\/\/dl.acm.org\/doi\/10.1109\/tkde.2022.3172903","DOI":"10.1109\/tkde.2022.3172903"},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"publisher","unstructured":"Xing Su Shan Xue Fanzhen Liu Jia Wu Jian Yang Chuan Zhou Wenbin Hu Cecile Paris Surya Nepal Di Jin Quan\u00a0Z. Sheng and Philip\u00a0S. Yu. 2024. A Comprehensive Survey on Community Detection With Deep Learning. IEEE Transactions on Neural Networks and Learning Systems 35 4 (April 2024) 4682\u20134702. 10.1109\/tnnls.2021.3137396","DOI":"10.1109\/tnnls.2021.3137396"},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"publisher","unstructured":"Xing Su Shan Xue Fanzhen Liu Jia Wu Jian Yang Chuan Zhou Wenbin Hu Cecile Paris Surya Nepal Di Jin Quan\u00a0Z. Sheng and Philip\u00a0S. Yu. 2024. A Comprehensive Survey on Community Detection With Deep Learning. IEEE Transactions on Neural Networks and Learning Systems 35 4 (2024) 4682\u20134702. 10.1109\/TNNLS.2021.3137396","DOI":"10.1109\/TNNLS.2021.3137396"},{"key":"e_1_3_3_2_13_2","first-page":"13308","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Tan Yanchao","year":"2023","unstructured":"Yanchao Tan, Zihao Zhou, Hang Lv, Weiming Liu, and Carl Yang. 2023. WalkLM: A Uniform Language Model Fine-tuning Framework for Attributed Graph Embedding. In Advances in Neural Information Processing Systems , A.\u00a0Oh, T.\u00a0Naumann, A.\u00a0Globerson, K.\u00a0Saenko, M.\u00a0Hardt, and S.\u00a0Levine (Eds.), Vol.\u00a036. Curran Associates, Inc., 13308\u201313325. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2023\/file\/2ac879d1865475a7abc8dfc7a9c15c27-Paper-Conference.pdf"},{"key":"e_1_3_3_2_14_2","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan\u00a0N. Gomez Lukasz Kaiser and Illia Polosukhin. 2023. Attention Is All You Need. arxiv:https:\/\/arXiv.org\/abs\/1706.03762\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/1706.03762"},{"key":"e_1_3_3_2_15_2","volume-title":"Advances in Neural Information Processing Systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan\u00a0N Gomez, \u0141\u00a0ukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems , I.\u00a0Guyon, U.\u00a0Von Luxburg, S.\u00a0Bengio, H.\u00a0Wallach, R.\u00a0Fergus, S.\u00a0Vishwanathan, and R.\u00a0Garnett (Eds.), Vol.\u00a030. Curran Associates, Inc.https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf https:\/\/dl.acm.org\/doi\/10.5555\/3295222.3295349"},{"key":"e_1_3_3_2_16_2","unstructured":"Felix Wu Tianyi Zhang Amauri\u00a0Holanda de Souza Jr.\u00a0au2 Christopher Fifty Tao Yu and Kilian\u00a0Q. Weinberger. 2019. Simplifying Graph Convolutional Networks. arxiv:https:\/\/arXiv.org\/abs\/1902.07153\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/1902.07153"},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"publisher","unstructured":"Carl Yang Yuxin Xiao Yu Zhang Yizhou Sun and Jiawei Han. 2020. Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark. IEEE Transactions on Knowledge and Data Engineering 34 10 (2020) 4854\u20134873. 10.1109\/TKDE.2020.2981334","DOI":"10.1109\/TKDE.2020.2981334"},{"key":"e_1_3_3_2_18_2","unstructured":"Jiawei Zhang Haopeng Zhang Congying Xia and Li Sun. 2020. Graph-Bert: Only Attention is Needed for Learning Graph Representations. arxiv:https:\/\/arXiv.org\/abs\/2001.05140\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2001.05140"},{"key":"e_1_3_3_2_19_2","unstructured":"Muhan Zhang and Yixin Chen. 2018. Link Prediction Based on Graph Neural Networks. arxiv:https:\/\/arXiv.org\/abs\/1802.09691\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/1802.09691"},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"publisher","unstructured":"Jie Zhou Ganqu Cui Shengding Hu Zhengyan Zhang Cheng Yang Zhiyuan Liu Lifeng Wang Changcheng Li and Maosong Sun. 2020. Graph neural networks: A review of methods and applications. AI Open 1 (2020) 57\u201381. 10.1016\/j.aiopen.2021.01.001","DOI":"10.1016\/j.aiopen.2021.01.001"}],"event":{"name":"NSysS '24: 11th International Conference on Networking, Systems, and Security","acronym":"NSysS '24","location":"Khulna Karak Bangladesh"},"container-title":["Proceedings of the 11th International Conference on Networking, Systems, and Security"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3704522.3704537","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3704522.3704537","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:16Z","timestamp":1750295896000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3704522.3704537"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,19]]},"references-count":19,"alternative-id":["10.1145\/3704522.3704537","10.1145\/3704522"],"URL":"https:\/\/doi.org\/10.1145\/3704522.3704537","relation":{},"subject":[],"published":{"date-parts":[[2024,12,19]]},"assertion":[{"value":"2025-01-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}