{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T01:12:23Z","timestamp":1777338743532,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":95,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["No. 2023YFF1205001"],"award-info":[{"award-number":["No. 2023YFF1205001"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 62222209, 62250008, 62102222, 62206149"],"award-info":[{"award-number":["No. 62222209, 62250008, 62102222, 62206149"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing National Research Center for Information Science and Technology under Grant","award":["No. BNR2023RC01003, BNR2023TD03006"],"award-info":[{"award-number":["No. BNR2023RC01003, BNR2023TD03006"]}]},{"name":"Beijing Key Lab of Networked Multimedia"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671709","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:54:55Z","timestamp":1724561695000},"page":"4350-4361","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":35,"title":["LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1329-1313","authenticated-orcid":false,"given":"Zeyang","family":"Zhang","sequence":"first","affiliation":[{"name":"DCST, Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0351-2939","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"DCST, BNRist, Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2451-843X","authenticated-orcid":false,"given":"Ziwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"DCST, Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3544-5563","authenticated-orcid":false,"given":"Haoyang","family":"Li","sequence":"additional","affiliation":[{"name":"DCST, Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0419-5226","authenticated-orcid":false,"given":"Yijian","family":"Qin","sequence":"additional","affiliation":[{"name":"DCST, Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2236-9290","authenticated-orcid":false,"given":"Wenwu","family":"Zhu","sequence":"additional","affiliation":[{"name":"DCST, BNRist, Tsinghua University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","first-page":"23716","article-title":"Flamingo: a visual language model for few-shot learning","volume":"35","author":"Alayrac Jean-Baptiste","year":"2022","unstructured":"Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al. 2022. Flamingo: a visual language model for few-shot learning. Advances in Neural Information Processing Systems, Vol. 35 (2022), 23716--23736.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_2_1","unstructured":"Jinze Bai Shuai Bai Yunfei Chu Zeyu Cui Kai Dang Xiaodong Deng Yang Fan Wenbin Ge Yu Han Fei Huang et al. 2023. Qwen technical report. arXiv preprint arXiv:2309.16609 (2023)."},{"key":"e_1_3_2_2_3_1","volume-title":"Representation learning: A review and new perspectives","author":"Bengio Yoshua","year":"2013","unstructured":"Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, Vol. 35, 8 (2013), 1798--1828."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i16.29720"},{"key":"e_1_3_2_2_5_1","unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell Sandhini Agarwal Ariel Herbert-Voss Gretchen Krueger Tom Henighan Rewon Child Aditya Ramesh Daniel Ziegler Jeffrey Wu Clemens Winter Chris Hesse Mark Chen Eric Sigler Mateusz Litwin Scott Gray Benjamin Chess Jack Clark Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever and Dario Amodei. 2020. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems. 1877--1901."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3481955"},{"key":"e_1_3_2_2_7_1","volume-title":"LLM4TS: Two-Stage Fine-Tuning for Time-Series Forecasting with Pre-Trained LLMs. arXiv preprint arXiv:2308.08469","author":"Chang Ching","year":"2023","unstructured":"Ching Chang, Wen-Chih Peng, and Tien-Fu Chen. 2023. LLM4TS: Two-Stage Fine-Tuning for Time-Series Forecasting with Pre-Trained LLMs. arXiv preprint arXiv:2308.08469 (2023)."},{"key":"e_1_3_2_2_8_1","volume-title":"Encode, Train and Interpret for Continuous-time Dynamic Graph Learning. arXiv preprint arXiv:2303.12341","author":"Chen Chao","year":"2023","unstructured":"Chao Chen, Haoyu Geng, Nianzu Yang, Xiaokang Yang, and Junchi Yan. 2023. EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning. arXiv preprint arXiv:2303.12341 (2023)."},{"key":"e_1_3_2_2_9_1","first-page":"26924","article-title":"Curriculum Disentangled Recommendation with Noisy Multi-feedback","volume":"34","author":"Chen Hong","year":"2021","unstructured":"Hong Chen, Yudong Chen, Xin Wang, Ruobing Xie, Rui Wang, Feng Xia, and Wenwu Zhu. 2021. Curriculum Disentangled Recommendation with Noisy Multi-feedback. NeurIPS, Vol. 34 (2021), 26924--26936.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"Hong Chen Xin Wang Yipeng Zhang Yuwei Zhou Zeyang Zhang Siao Tang and Wenwu Zhu. 2024. DisenStudio: Customized Multi-subject Text-to-Video Generation with Disentangled Spatial Control. arxiv: 2405.12796 [cs.CV]","DOI":"10.1145\/3664647.3680637"},{"key":"e_1_3_2_2_11_1","volume-title":"The Twelfth International Conference on Learning Representations.","author":"Chen Hong","year":"2023","unstructured":"Hong Chen, Yipeng Zhang, Simin Wu, Xin Wang, Xuguang Duan, Yuwei Zhou, and Wenwu Zhu. 2023. Disenbooth: Identity-preserving disentangled tuning for subject-driven text-to-image generation. In The Twelfth International Conference on Learning Representations."},{"key":"e_1_3_2_2_12_1","volume-title":"NeurIPS","volume":"29","author":"Chen Xi","year":"2016","unstructured":"Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2016. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. NeurIPS, Vol. 29 (2016)."},{"key":"e_1_3_2_2_13_1","volume-title":"Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs. arXiv preprint arXiv:2307.03393","author":"Chen Zhikai","year":"2023","unstructured":"Zhikai Chen, Haitao Mao, Hang Li, Wei Jin, Hongzhi Wen, Xiaochi Wei, Shuaiqiang Wang, Dawei Yin, Wenqi Fan, Hui Liu, and Jiliang Tang. 2023. Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs. arXiv preprint arXiv:2307.03393 (2023)."},{"key":"e_1_3_2_2_14_1","volume-title":"Xing","author":"Chiang Wei-Lin","year":"2023","unstructured":"Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. 2023. Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality. https:\/\/lmsys.org\/blog\/2023-03--30-vicuna\/"},{"key":"e_1_3_2_2_15_1","volume-title":"Dynamic Graph Representation Learning via Graph Transformer Networks. arXiv preprint","author":"Cong Weilin","year":"2021","unstructured":"Weilin Cong, Yanhong Wu, Yuandong Tian, Mengting Gu, Yinglong Xia, Mehrdad Mahdavi, and Chun-cheng Jason Chen. 2021. Dynamic Graph Representation Learning via Graph Transformer Networks. arXiv preprint (2021)."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403209"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.26"},{"key":"e_1_3_2_2_18_1","unstructured":"Paul ErdHos Alfr\u00e9d R\u00e9nyi et al. 1960. On the evolution of random graphs. Publ. math. inst. hung. acad. sci Vol. 5 1 (1960) 17--60."},{"key":"e_1_3_2_2_19_1","volume-title":"Talk like a graph: Encoding graphs for large language models. ICLR","author":"Fatemi Bahare","year":"2024","unstructured":"Bahare Fatemi, Jonathan Halcrow, and Bryan Perozzi. 2024. Talk like a graph: Encoding graphs for large language models. ICLR (2024)."},{"key":"e_1_3_2_2_20_1","volume-title":"LLM4VG: Large Language Models Evaluation for Video Grounding. arXiv preprint arXiv:2312.14206","author":"Feng Wei","year":"2023","unstructured":"Wei Feng, Xin Wang, Hong Chen, Zeyang Zhang, Zihan Song, Yuwei Zhou, and Wenwu Zhu. 2023. LLM4VG: Large Language Models Evaluation for Video Grounding. arXiv preprint arXiv:2312.14206 (2023)."},{"key":"e_1_3_2_2_21_1","volume-title":"AutoGL: A Library for Automated Graph Learning. In ICLR 2021 Workshop GTRL.","author":"Guan Chaoyu","year":"2021","unstructured":"Chaoyu Guan, Ziwei Zhang, Haoyang Li, Heng Chang, Zeyang Zhang, Yijian Qin, Jiyan Jiang, Xin Wang, and Wenwu Zhu. 2021. AutoGL: A Library for Automated Graph Learning. In ICLR 2021 Workshop GTRL."},{"key":"e_1_3_2_2_22_1","volume-title":"GPT4Graph: Can Large Language Models Understand Graph Structured Data? An Empirical Evaluation and Benchmarking. arXiv preprint arXiv:2305.15066","author":"Guo Jiayan","year":"2023","unstructured":"Jiayan Guo, Lun Du, and Hengyu Liu. 2023. GPT4Graph: Can Large Language Models Understand Graph Structured Data? An Empirical Evaluation and Benchmarking. arXiv preprint arXiv:2305.15066 (2023)."},{"key":"e_1_3_2_2_23_1","volume-title":"Variational graph recurrent neural networks. Advances in neural information processing systems","author":"Hajiramezanali Ehsan","year":"2019","unstructured":"Ehsan Hajiramezanali, Arman Hasanzadeh, Krishna Narayanan, Nick Duffield, Mingyuan Zhou, and Xiaoning Qian. 2019. Variational graph recurrent neural networks. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_24_1","volume-title":"Explanations as Features: LLM-Based Features for Text-Attributed Graphs. arXiv preprint arXiv:2305.19523","author":"He Xiaoxin","year":"2023","unstructured":"Xiaoxin He, Xavier Bresson, Thomas Laurent, and Bryan Hooi. 2023. Explanations as Features: LLM-Based Features for Text-Attributed Graphs. arXiv preprint arXiv:2305.19523 (2023)."},{"key":"e_1_3_2_2_25_1","volume-title":"Kathryn Blackmond Laskey, and Samuel Leinhardt","author":"Holland Paul W","year":"1983","unstructured":"Paul W Holland, Kathryn Blackmond Laskey, and Samuel Leinhardt. 1983. Stochastic blockmodels: First steps. Social networks, Vol. 5, 2 (1983), 109--137."},{"key":"e_1_3_2_2_26_1","volume-title":"Learning to decompose and disentangle representations for video prediction. Advances in neural information processing systems","author":"Hsieh Jun-Ting","year":"2018","unstructured":"Jun-Ting Hsieh, Bingbin Liu, De-An Huang, Li F Fei-Fei, and Juan Carlos Niebles. 2018. Learning to decompose and disentangle representations for video prediction. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_2_27_1","volume-title":"Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al.","author":"Jiang Albert Q","year":"2023","unstructured":"Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023. Mistral 7B. arXiv preprint arXiv:2310.06825 (2023)."},{"key":"e_1_3_2_2_28_1","volume-title":"Wayne Xin Zhao, and Ji-Rong Wen","author":"Jiang Jinhao","year":"2023","unstructured":"Jinhao Jiang, Kun Zhou, Zican Dong, Keming Ye, Wayne Xin Zhao, and Ji-Rong Wen. 2023. Structgpt: A general framework for large language model to reason over structured data. arXiv preprint arXiv:2305.09645 (2023)."},{"key":"e_1_3_2_2_29_1","volume-title":"Large language models on graphs: A comprehensive survey. arXiv preprint arXiv:2312.02783","author":"Jin Bowen","year":"2023","unstructured":"Bowen Jin, Gang Liu, Chi Han, Meng Jiang, Heng Ji, and Jiawei Han. 2023. Large language models on graphs: A comprehensive survey. arXiv preprint arXiv:2312.02783 (2023)."},{"key":"e_1_3_2_2_30_1","unstructured":"Takeshi Kojima et al. 2022. Large Language Models are Zero-Shot Reasoners. arXiv preprint arXiv:2205.11916 (2022)."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/1217299.1217301"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330827"},{"key":"e_1_3_2_2_33_1","volume-title":"Disentangled Graph Self-supervised Learning for Out-of-Distribution Generalization. In International conference on machine learning. PMLR.","author":"Li Haoyang","year":"2024","unstructured":"Haoyang Li, Xin Wang, Zeyang Zhang, Haibo Chen, Ziwei Zhang, and Wenwu Zhu. 2024. Disentangled Graph Self-supervised Learning for Out-of-Distribution Generalization. In International conference on machine learning. PMLR."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3050571"},{"key":"e_1_3_2_2_35_1","first-page":"21872","article-title":"Disentangled contrastive learning on graphs","volume":"34","author":"Li Haoyang","year":"2021","unstructured":"Haoyang Li, Xin Wang, Ziwei Zhang, Zehuan Yuan, Hang Li, and Wenwu Zhu. 2021. Disentangled contrastive learning on graphs. Advances in Neural Information Processing Systems, Vol. 34 (2021), 21872--21884.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3206875"},{"key":"e_1_3_2_2_37_1","volume-title":"LLM-Enhanced Causal Discovery in Temporal Domain from Interventional Data. arXiv:2404.14786","author":"Li Peiwen","year":"2024","unstructured":"Peiwen Li, Xin Wang, Zeyang Zhang, Yuan Meng, Fang Shen, Yue Li, Jialong Wang, Yang Li, and Wenweu Zhu. 2024. LLM-Enhanced Causal Discovery in Temporal Domain from Interventional Data. arXiv:2404.14786 (2024)."},{"key":"e_1_3_2_2_38_1","unstructured":"Peiwen Li Xin Wang Zeyang Zhang Yijian Qin Ziwei Zhang Jialong Wang Yang Li and Wenwu Zhu. 2024. Causal-Aware Graph Neural Architecture Search under Distribution Shifts. arxiv: 2405.16489 [cs.LG]"},{"key":"e_1_3_2_2_39_1","volume-title":"A survey of graph meets large language model: Progress and future directions. arXiv preprint arXiv:2311.12399","author":"Li Yuhan","year":"2023","unstructured":"Yuhan Li, Zhixun Li, Peisong Wang, Jia Li, Xiangguo Sun, Hong Cheng, and Jeffrey Xu Yu. 2023. A survey of graph meets large language model: Progress and future directions. arXiv preprint arXiv:2311.12399 (2023)."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5929"},{"key":"e_1_3_2_2_41_1","volume-title":"LLM-Rec: Personalized Recommendation via Prompting Large Language Models. arXiv preprint arXiv:2307.15780","author":"Lyu Hanjia","year":"2023","unstructured":"Hanjia Lyu, Song Jiang, Hanqing Zeng, Yinglong Xia, and Jiebo Luo. 2023. LLM-Rec: Personalized Recommendation via Prompting Large Language Models. arXiv preprint arXiv:2307.15780 (2023)."},{"key":"e_1_3_2_2_42_1","unstructured":"Jianxin Ma Peng Cui Kun Kuang Xin Wang and Wenwu Zhu. 2019. Disentangled graph convolutional networks. In ICML. PMLR 4212--4221."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00018"},{"key":"e_1_3_2_2_44_1","volume-title":"What in-context learning \"learns\" in-context: Disentangling task recognition and task learning. Ph.,D. Dissertation","author":"Pan Jane","unstructured":"Jane Pan. 2023. What in-context learning \"learns\" in-context: Disentangling task recognition and task learning. Ph.,D. Dissertation. Princeton University."},{"key":"e_1_3_2_2_45_1","unstructured":"Yijian Qin Xin Wang Zeyang Zhang and Wenwu Zhu. 2021. Graph differentiable architecture search with structure learning. In NeurIPS."},{"key":"e_1_3_2_2_46_1","volume-title":"Disentangled representation learning with large language models for text-attributed graphs. arXiv preprint arXiv:2310.18152","author":"Qin Yijian","year":"2023","unstructured":"Yijian Qin, Xin Wang, Ziwei Zhang, and Wenwu Zhu. 2023. Disentangled representation learning with large language models for text-attributed graphs. arXiv preprint arXiv:2310.18152 (2023)."},{"key":"e_1_3_2_2_47_1","unstructured":"Yijian Qin Ziwei Zhang Xin Wang Zeyang Zhang and Wenwu Zhu. 2022. NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_48_1","volume-title":"Temporal graph networks for deep learning on dynamic graphs. arXiv preprint arXiv:2006.10637","author":"Rossi Emanuele","year":"2020","unstructured":"Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein. 2020. Temporal graph networks for deep learning on dynamic graphs. arXiv preprint arXiv:2006.10637 (2020)."},{"key":"e_1_3_2_2_49_1","unstructured":"Baptiste Rozi\u00e8re Jonas Gehring Fabian Gloeckle Sten Sootla Itai Gat Xiaoqing Ellen Tan Yossi Adi Jingyu Liu Tal Remez J\u00e9r\u00e9my Rapin Artyom Kozhevnikov Ivan Evtimov Joanna Bitton Manish Bhatt Cristian Canton Ferrer Aaron Grattafiori Wenhan Xiong Alexandre D\u00e9fossez Jade Copet Faisal Azhar Hugo Touvron Louis Martin Nicolas Usunier Thomas Scialom and Gabriel Synnaeve. 2023. Code Llama: Open Foundation Models for Code. arxiv: 2308.12950 [cs.CL]"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371845"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN.2014.6846743"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-04167-0_33"},{"key":"e_1_3_2_2_53_1","first-page":"120","article-title":"The Enron email dataset database schema and brief statistical report. Information sciences institute technical report","volume":"4","author":"Shetty Jitesh","year":"2004","unstructured":"Jitesh Shetty and Jafar Adibi. 2004. The Enron email dataset database schema and brief statistical report. Information sciences institute technical report, University of Southern California, Vol. 4, 1 (2004), 120--128.","journal-title":"University of Southern California"},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3082932"},{"key":"e_1_3_2_2_55_1","volume-title":"TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series. arXiv preprint arXiv:2308.08241","author":"Sun Chenxi","year":"2023","unstructured":"Chenxi Sun, Yaliang Li, Hongyan Li, and Shenda Hong. 2023. TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series. arXiv preprint arXiv:2308.08241 (2023)."},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16563"},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1402008"},{"key":"e_1_3_2_2_58_1","volume-title":"Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan, and Daniel Shu Wei Ting.","author":"Thirunavukarasu Arun James","year":"2023","unstructured":"Arun James Thirunavukarasu, Darren Shu Jeng Ting, Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan, and Daniel Shu Wei Ting. 2023. Large language models in medicine. Nature medicine, Vol. 29, 8 (2023), 1930--1940."},{"key":"e_1_3_2_2_59_1","volume-title":"Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971","author":"Touvron Hugo","year":"2023","unstructured":"Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth\u00e9e Lacroix, Baptiste Rozi\u00e8re, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)."},{"key":"e_1_3_2_2_60_1","volume-title":"Can Language Models Solve Graph Problems in Natural Language? arXiv preprint arXiv:2305.10037","author":"Wang Heng","year":"2023","unstructured":"Heng Wang, Shangbin Feng, Tianxing He, Zhaoxuan Tan, Xiaochuang Han, and Yulia Tsvetkov. 2023. Can Language Models Solve Graph Problems in Natural Language? arXiv preprint arXiv:2305.10037 (2023)."},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"crossref","unstructured":"Lei Wang Chen Ma Xueyang Feng Zeyu Zhang Hao Yang Jingsen Zhang Zhiyuan Chen Jiakai Tang Xu Chen Yankai Lin et al. 2023. A survey on large language model based autonomous agents. arXiv preprint arXiv:2308.11432 (2023).","DOI":"10.1007\/s11704-024-40231-1"},{"key":"e_1_3_2_2_62_1","unstructured":"Xuezhi Wang et al. 2022. Self-Consistency Improves Chain of Thought Reasoning in Language Models. arXiv preprint arXiv:2203.11171 (2022)."},{"key":"e_1_3_2_2_63_1","unstructured":"Xin Wang Hong Chen Si'ao Tang Zihao Wu and Wenwu Zhu. 2023. Disentangled Representation Learning. arxiv: 2211.11695 [cs.LG]"},{"key":"e_1_3_2_2_64_1","volume-title":"Disentangled Representation Learning for Recommendation","author":"Wang Xin","year":"2022","unstructured":"Xin Wang, Hong Chen, Yuwei Zhou, Jianxin Ma, and Wenwu Zhu. 2022. Disentangled Representation Learning for Recommendation. IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)."},{"key":"e_1_3_2_2_65_1","volume-title":"International Conference on Machine Learning. PMLR, 36174--36192","author":"Wang Xin","year":"2023","unstructured":"Xin Wang, Zirui Pan, Yuwei Zhou, Hong Chen, Chendi Ge, and Wenwu Zhu. 2023 d. Curriculum co-disentangled representation learning across multiple environments for social recommendation. In International Conference on Machine Learning. PMLR, 36174--36192."},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"crossref","unstructured":"Xin Wang Zihao Wu Hong Chen Xiaohan Lan and Wenwu Zhu. 2023 e. Mixup-Augmented Temporally Debiased Video Grounding with Content-Location Disentanglement. (2023).","DOI":"10.1145\/3581783.3612401"},{"key":"e_1_3_2_2_67_1","volume-title":"Inductive representation learning in temporal networks via causal anonymous walks. arXiv preprint arXiv:2101.05974","author":"Wang Yanbang","year":"2021","unstructured":"Yanbang Wang, Yen-Yu Chang, Yunyu Liu, Jure Leskovec, and Pan Li. 2021. Inductive representation learning in temporal networks via causal anonymous walks. arXiv preprint arXiv:2101.05974 (2021)."},{"key":"e_1_3_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450096"},{"key":"e_1_3_2_2_69_1","first-page":"24824","article-title":"Chain-of-thought prompting elicits reasoning in large language models","volume":"35","author":"Wei Jason","year":"2022","unstructured":"Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022. Chain-of-thought prompting elicits reasoning in large language models. NeurIPS, Vol. 35 (2022), 24824--24837.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_70_1","volume-title":"Jackie Chi Kit Cheung, and William L Hamilton","author":"Wu Jiapeng","year":"2020","unstructured":"Jiapeng Wu, Meng Cao, Jackie Chi Kit Cheung, and William L Hamilton. 2020. Temp: Temporal message passing for temporal knowledge graph completion. arXiv preprint arXiv:2010.03526 (2020)."},{"key":"e_1_3_2_2_71_1","volume-title":"Inductive representation learning on temporal graphs. arXiv preprint arXiv:2002.07962","author":"Xu Da","year":"2020","unstructured":"Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2020. Inductive representation learning on temporal graphs. arXiv preprint arXiv:2002.07962 (2020)."},{"key":"e_1_3_2_2_72_1","unstructured":"Aiyuan Yang Bin Xiao Bingning Wang Borong Zhang Ce Bian Chao Yin Chenxu Lv Da Pan Dian Wang Dong Yan et al. 2023. Baichuan 2: Open large-scale language models. arXiv preprint arXiv:2309.10305 (2023)."},{"key":"e_1_3_2_2_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467422"},{"key":"e_1_3_2_2_74_1","first-page":"20286","article-title":"Factorizable graph convolutional networks","volume":"33","author":"Yang Yiding","year":"2020","unstructured":"Yiding Yang, Zunlei Feng, Mingli Song, and Xinchao Wang. 2020. Factorizable graph convolutional networks. Advances in Neural Information Processing Systems, Vol. 33 (2020), 20286--20296.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_75_1","volume-title":"Exploring the Potential of Large Language Models in Graph Generation. arXiv preprint arXiv:2403.14358","author":"Yao Yang","year":"2024","unstructured":"Yang Yao, Xin Wang, Zeyang Zhang, Yijian Qin, Ziwei Zhang, Xu Chu, Yuekui Yang, Wenwu Zhu, and Hong Mei. 2024. Exploring the Potential of Large Language Models in Graph Generation. arXiv preprint arXiv:2403.14358 (2024)."},{"key":"e_1_3_2_2_76_1","volume-title":"Natural Language is All a Graph Needs. arXiv preprint arXiv:2308.07134","author":"Ye Ruosong","year":"2023","unstructured":"Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu, and Yongfeng Zhang. 2023. Natural Language is All a Graph Needs. arXiv preprint arXiv:2308.07134 (2023)."},{"key":"e_1_3_2_2_77_1","unstructured":"Hong Chen Jiapei Fan Weigao Wen Hui Xue Hong Mei Wenwu Zhu Yipeng Zhang Xin Wang. 2024. Large Language Model With Curriculum Reasoning for Visual Concept Recognition. In ACM SIGKDD."},{"key":"e_1_3_2_2_78_1","doi-asserted-by":"crossref","unstructured":"Jiaxuan You Yichen Wang Aditya Pal Pong Eksombatchai Chuck Rosenburg and Jure Leskovec. 2019. Hierarchical temporal convolutional networks for dynamic recommender systems. In The world wide web conference. 2236--2246.","DOI":"10.1145\/3308558.3313747"},{"key":"e_1_3_2_2_79_1","volume-title":"Temporal Data Meets LLM--Explainable Financial Time Series Forecasting. arXiv preprint arXiv:2306.11025","author":"Yu Xinli","year":"2023","unstructured":"Xinli Yu, Zheng Chen, Yuan Ling, Shujing Dong, Zongyi Liu, and Yanbin Lu. 2023. Temporal Data Meets LLM--Explainable Financial Time Series Forecasting. arXiv preprint arXiv:2306.11025 (2023)."},{"key":"e_1_3_2_2_80_1","volume-title":"Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT. arXiv:2304.11116","author":"Zhang Jiawei","year":"2023","unstructured":"Jiawei Zhang. 2023. Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT. arXiv:2304.11116 (2023)."},{"key":"e_1_3_2_2_81_1","volume-title":"NeurIPS 2023 Workshop: New Frontiers in Graph Learning.","author":"Zhang Ziwei","year":"2023","unstructured":"Ziwei Zhang, Haoyang Li, Zeyang Zhang, Yijian Qin, Xin Wang, and Wenwu Zhu. 2023. Graph meets llms: Towards large graph models. In NeurIPS 2023 Workshop: New Frontiers in Graph Learning."},{"key":"e_1_3_2_2_82_1","volume-title":"Large Graph Models: A Perspective. Advances in Neural Information Processing Systems GLFrontiers Workshop","author":"Zhang Ziwei","year":"2023","unstructured":"Ziwei Zhang, Haoyang Li, Zeyang Zhang, Yijian Qin, Xin Wang, and Wenwu Zhu. 2023. Large Graph Models: A Perspective. Advances in Neural Information Processing Systems GLFrontiers Workshop (2023)."},{"key":"e_1_3_2_2_83_1","volume-title":"Out-of-Distribution Generalized Dynamic Graph Neural Network for Human Albumin Prediction. In IEEE International Conference on Medical Artificial Intelligence.","author":"Zhang Zeyang","year":"2023","unstructured":"Zeyang Zhang, Xingwang Li, Fei Teng, Ning Lin, Xueling Zhu, Xin Wang, and Wenwu Zhu. 2023. Out-of-Distribution Generalized Dynamic Graph Neural Network for Human Albumin Prediction. In IEEE International Conference on Medical Artificial Intelligence."},{"key":"e_1_3_2_2_84_1","volume-title":"Disentangled Continual Graph Neural Architecture Search with Invariant Modularization. In International Conference on Machine Learning.","author":"Zhang Zeyang","year":"2024","unstructured":"Zeyang Zhang, Xin Wang, Yijian Qin, Hong Chen, Ziwei Zhang, Xu Chu, and Wenwu Zhu. 2024. Disentangled Continual Graph Neural Architecture Search with Invariant Modularization. In International Conference on Machine Learning."},{"key":"e_1_3_2_2_85_1","unstructured":"Zeyang Zhang Xin Wang Ziwei Zhang Haoyang Li Zhou Qin and Wenwu Zhu. 2022. Dynamic graph neural networks under spatio-temporal distribution shift. In NeurIPS."},{"key":"e_1_3_2_2_86_1","volume-title":"2023 d. Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotion. arXiv preprint arXiv:2311.14255","author":"Zhang Zeyang","year":"2023","unstructured":"Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, and Wenwu Zhu. 2023 d. Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotion. arXiv preprint arXiv:2311.14255 (2023)."},{"key":"e_1_3_2_2_87_1","unstructured":"Zeyang Zhang Xin Wang Ziwei Zhang Zhou Qin Weigao Wen Hui Xue Haoyang Li and Wenwu Zhu. 2023 e. Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts. In NeurIPS."},{"key":"e_1_3_2_2_88_1","unstructured":"Zeyang Zhang Xin Wang Ziwei Zhang Guangyao Shen Shiqi Shen and Wenwu Zhu. 2023 f. Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_89_1","volume-title":"Automatic chain of thought prompting in large language models. arXiv:2210.03493","author":"Zhang Zhuosheng","year":"2022","unstructured":"Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. 2022. Automatic chain of thought prompting in large language models. arXiv:2210.03493 (2022)."},{"key":"e_1_3_2_2_90_1","volume-title":"2023 g. Multimodal chain-of-thought reasoning in language models. arXiv preprint arXiv:2302.00923","author":"Zhang Zhuosheng","year":"2023","unstructured":"Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, and Alex Smola. 2023 g. Multimodal chain-of-thought reasoning in language models. arXiv preprint arXiv:2302.00923 (2023)."},{"key":"e_1_3_2_2_91_1","volume-title":"Dynamic Heterogeneous Graph Attention Neural Architecture Search. In Thirty-Seventh AAAI Conference on Artificial Intelligence.","author":"Zhang Zeyang","year":"2023","unstructured":"Zeyang Zhang, Ziwei Zhang, Xin Wang, Yijian Qin, Zhou Qin, and Wenwu Zhu. 2023 h. Dynamic Heterogeneous Graph Attention Neural Architecture Search. In Thirty-Seventh AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_2_92_1","volume-title":"Learning to Solve Travelling Salesman Problem with Hardness-adaptive Curriculum. arXiv preprint arXiv:2204.03236","author":"Zhang Zeyang","year":"2022","unstructured":"Zeyang Zhang, Ziwei Zhang, Xin Wang, and Wenwu Zhu. 2022. Learning to Solve Travelling Salesman Problem with Hardness-adaptive Curriculum. arXiv preprint arXiv:2204.03236 (2022)."},{"key":"e_1_3_2_2_93_1","unstructured":"Denny Zhou et al. 2023. Teaching Small Language Models to Reason. arXiv preprint arXiv:2301.09208 (2023)."},{"key":"e_1_3_2_2_94_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11257"},{"key":"e_1_3_2_2_95_1","volume-title":"Learnable Encoder-Decoder Architecture for Dynamic Graph: A Survey. arXiv preprint arXiv:2203.10480","author":"Zhu Yuecai","year":"2022","unstructured":"Yuecai Zhu, Fuyuan Lyu, Chengming Hu, Xi Chen, and Xue Liu. 2022. Learnable Encoder-Decoder Architecture for Dynamic Graph: A Survey. arXiv preprint arXiv:2203.10480 (2022)."}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671709","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671709","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:06:00Z","timestamp":1750291560000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671709"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":95,"alternative-id":["10.1145\/3637528.3671709","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671709","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}