{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T09:58:48Z","timestamp":1775815128641,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":71,"publisher":"ACM","funder":[{"DOI":"10.13039\/501100006374","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["NJ2024029"],"award-info":[{"award-number":["NJ2024029"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006374","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176137"],"award-info":[{"award-number":["62176137"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"name":"SMP-IDATA Open Youth Fund","award":["SMP2023-iData-007"],"award-info":[{"award-number":["SMP2023-iData-007"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,7,13]]},"DOI":"10.1145\/3726302.3730085","type":"proceedings-article","created":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T01:18:36Z","timestamp":1752455916000},"page":"1995-2005","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Social Context-Aware Community-Level Propagation Prediction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6174-1045","authenticated-orcid":false,"given":"Jinfei","family":"Gao","sequence":"first","affiliation":[{"name":"Shandong University, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4879-2169","authenticated-orcid":false,"given":"Xiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3197-5698","authenticated-orcid":false,"given":"Tian","family":"Gan","sequence":"additional","affiliation":[{"name":"Shandong University, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4611-2986","authenticated-orcid":false,"given":"Jianhua","family":"Yin","sequence":"additional","affiliation":[{"name":"Shandong University, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4360-7035","authenticated-orcid":false,"given":"Chuanchen","family":"Luo","sequence":"additional","affiliation":[{"name":"Shandong University, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1476-0273","authenticated-orcid":false,"given":"Liqiang","family":"Nie","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,7,13]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Galileo AI. 2024. A Ranking & Evaluation Framework For LLM Hallucinations. https:\/\/www.galileo.ai\/hallucinationindex\/meta-llama-31--8b-instruct"},{"key":"e_1_3_2_1_2_1","volume-title":"International Conference on Cognition and Recongition. Springer, 67--78","author":"Bakkialakshmi VS","year":"2021","unstructured":"VS Bakkialakshmi and T Sudalaimuthu. 2021. Anomaly detection in social media using text-mining and emotion classification with emotion detection. In International Conference on Cognition and Recongition. Springer, 67--78."},{"key":"e_1_3_2_1_3_1","volume-title":"User cold-start recommendation via inductive heterogeneous graph neural network. ACM Transactions on Information Systems","author":"Cai Desheng","year":"2023","unstructured":"Desheng Cai, Shengsheng Qian, Quan Fang, Jun Hu, and Changsheng Xu. 2023. User cold-start recommendation via inductive heterogeneous graph neural network. ACM Transactions on Information Systems (2023), 1--27."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671824"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371834"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.14778\/3603581.3603595"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Zhikai Chen Haitao Mao Hang Li Wei Jin Hongzhi Wen Xiaochi Wei Shuaiqiang Wang Dawei Yin Wenqi Fan Hui Liu et al. 2024. Exploring the potential of large language models (llms) in learning on graphs. ACM SIGKDD Explorations Newsletter (2024) 42--61.","DOI":"10.1145\/3655103.3655110"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3672041"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657834"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557661"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467266"},{"key":"e_1_3_2_1_12_1","unstructured":"Abhimanyu Dubey Abhinav Jauhri Abhinav Pandey Abhishek Kadian Ahmad Al-Dahle Aiesha Letman Akhil Mathur Alan Schelten Amy Yang Angela Fan et al. 2024. The llama 3 herd of models. arXiv preprint arXiv:2407.21783 (2024)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539054"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741667"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01423"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612120"},{"key":"e_1_3_2_1_17_1","volume-title":"Predict then propagate: Graph neural networks meet personalized pagerank. arXiv preprint arXiv:1810.05997","author":"Gasteiger Johannes","year":"2018","unstructured":"Johannes Gasteiger, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2018. Predict then propagate: Graph neural networks meet personalized pagerank. arXiv preprint arXiv:1810.05997 (2018)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CCAA.2017.8229786"},{"key":"e_1_3_2_1_19_1","volume-title":"Inductive representation learning on large graphs. Advances in neural information processing systems","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems (2017)."},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence. 3515--3521","author":"He Dongxiao","year":"2021","unstructured":"Dongxiao He, Yue Song, Di Jin, Zhiyong Feng, Binbin Zhang, Zhizhi Yu, and Weixiong Zhang. 2021. Community-centric graph convolutional network for unsupervised community detection. In Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence. 3515--3521."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3073867"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657865"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591732"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599281"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599470"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i5.20517"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657801"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557387"},{"key":"e_1_3_2_1_31_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_32_1","unstructured":"Patrick Lewis Ethan Perez Aleksandra Piktus Fabio Petroni Vladimir Karpukhin Naman Goyal Heinrich K\u00fcttler Mike Lewis Wen-tau Yih Tim Rockt\u00e4schel et al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems (2020) 9459--9474."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615231"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Jiakang Li Songning Lai Zhihao Shuai Yuan Tan Yifan Jia Mianyang Yu Zichen Song Xiaokang Peng Ziyang Xu Yongxin Ni et al. 2024. A comprehensive review of community detection in graphs. Neurocomputing (2024) 128169.","DOI":"10.1016\/j.neucom.2024.128169"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371786"},{"key":"e_1_3_2_1_36_1","volume-title":"Videochat: Chat-centric video understanding. arXiv preprint arXiv:2305.06355","author":"Li KunChang","year":"2023","unstructured":"KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao. 2023a. Videochat: Chat-centric video understanding. arXiv preprint arXiv:2305.06355 (2023)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3592078"},{"key":"e_1_3_2_1_38_1","volume-title":"Caiming Xiong, Yingbo Zhou, and Semih Yavuz.","author":"Meng Rui","year":"2024","unstructured":"Rui Meng, Ye Liu, Shafiq Rayhan Joty, Caiming Xiong, Yingbo Zhou, and Semih Yavuz. 2024. SFR-Embedding-2: Advanced Text Embedding with Multi-stage Training."},{"key":"e_1_3_2_1_39_1","volume-title":"Proceedings of the International Conference on World Wide Web. ACM, 493--498","author":"Myers Seth A.","year":"2014","unstructured":"Seth A. Myers, Aneesh Sharma, Pankaj Gupta, and Jimmy Lin. 2014. Information network or social network? the structure of the twitter follow graph. In Proceedings of the International Conference on World Wide Web. ACM, 493--498."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512128"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539228"},{"key":"e_1_3_2_1_42_1","volume-title":"Long Context Transfer from Language to Vision. arXiv preprint arXiv:2406.16852","author":"Peiyuan Zhang","year":"2024","unstructured":"Zhang Peiyuan, Zhang Kaichen, Li Bo, Zeng Guangtao, Yang Jingkang, Zhang Yuanhan, Wang Ziyue, Tan Haoran, Li Chunyuan, and Ziwei Liu. 2024. Long Context Transfer from Language to Vision. arXiv preprint arXiv:2406.16852 (2024)."},{"key":"e_1_3_2_1_43_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_1_44_1","volume-title":"Masked label prediction: Unified message passing model for semi-supervised classification. arXiv preprint arXiv:2009.03509","author":"Shi Yunsheng","year":"2020","unstructured":"Yunsheng Shi, Zhengjie Huang, Shikun Feng, Hui Zhong, Wenjin Wang, and Yu Sun. 2020. Masked label prediction: Unified message passing model for semi-supervised classification. arXiv preprint arXiv:2009.03509 (2020)."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539192"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570478"},{"key":"e_1_3_2_1_47_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Veli\u010dkovi\u0107 Petar","year":"2017","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657851"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462929"},{"key":"e_1_3_2_1_50_1","volume-title":"ICASSP 2024--2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","author":"Wang Wenbo","unstructured":"Wenbo Wang, Ben Chen, Bingquan Liu, Xinxin Wang, Luwei Yang, Wen Jiang, Wei Ning, and Jian Guan. 2024. Mutual Information Assisted Graph Convolution Network for Cold-Start Recommendation. In ICASSP 2024--2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 6785--6789."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548098"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612152"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512083"},{"key":"e_1_3_2_1_55_1","volume-title":"Video DataFlywheel: Resolving the Impossible Data Trinity in Video-Language Understanding","author":"Wang Xiao","year":"2025","unstructured":"Xiao Wang, Jianlong Wu, Zijia Lin, Fuzheng Zhang, Di Zhang, and Liqiang Nie. 2025. Video DataFlywheel: Resolving the Impossible Data Trinity in Video-Language Understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence (2025)."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531851"},{"key":"e_1_3_2_1_57_1","volume-title":"A topological perspective on demystifying gnn-based link prediction performance. arXiv preprint arXiv:2310.04612","author":"Wang Yu","year":"2023","unstructured":"Yu Wang, Tong Zhao, Yuying Zhao, Yunchao Liu, Xueqi Cheng, Neil Shah, and Tyler Derr. 2023b. A topological perspective on demystifying gnn-based link prediction performance. arXiv preprint arXiv:2310.04612 (2023)."},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591641"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599298"},{"key":"e_1_3_2_1_60_1","volume-title":"Less is More: on the Over-Globalizing Problem in Graph Transformers. arXiv preprint arXiv:2405.01102","author":"Xing Yujie","year":"2024","unstructured":"Yujie Xing, Xiao Wang, Yibo Li, Hai Huang, and Chuan Shi. 2024. Less is More: on the Over-Globalizing Problem in Graph Transformers. arXiv preprint arXiv:2405.01102 (2024)."},{"key":"e_1_3_2_1_61_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_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3664647.3681630"},{"key":"e_1_3_2_1_63_1","volume-title":"arXiv preprint arXiv:2412.15115","author":"Yang An","year":"2024","unstructured":"An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, and Zihan Qiu. 2024. Qwen2.5 Technical Report. arXiv preprint arXiv:2412.15115 (2024)."},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531930"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539277"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657777"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475259"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645682"},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671913"},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657929"},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3672024"}],"event":{"name":"SIGIR '25: The 48th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Padua Italy","acronym":"SIGIR '25","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3726302.3730085","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T18:32:34Z","timestamp":1755887554000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3726302.3730085"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,13]]},"references-count":71,"alternative-id":["10.1145\/3726302.3730085","10.1145\/3726302"],"URL":"https:\/\/doi.org\/10.1145\/3726302.3730085","relation":{},"subject":[],"published":{"date-parts":[[2025,7,13]]},"assertion":[{"value":"2025-07-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}