{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T11:56:31Z","timestamp":1784548591612,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":41,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key R&D Program of China","award":["2019YFA0707204"],"award-info":[{"award-number":["2019YFA0707204"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Nos. 62176014"],"award-info":[{"award-number":["Nos. 62176014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,17]]},"DOI":"10.1145\/3511808.3557398","type":"proceedings-article","created":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T01:29:57Z","timestamp":1665883797000},"page":"572-581","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction"],"prefix":"10.1145","author":[{"given":"Hao","family":"Geng","sequence":"first","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deqing","family":"Wang","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuzhen","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuehua","family":"Ming","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenguang","family":"Du","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Jiang","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haolong","family":"Guo","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Liu","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.joi.2019.02.011"},{"key":"e_1_3_2_2_2_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=F72ximsx7C1","author":"Brody Shaked","year":"2022","unstructured":"Shaked Brody , Uri Alon , and Eran Yahav . 2022 . How Attentive are Graph Attention Networks? . In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=F72ximsx7C1 Shaked Brody, Uri Alon, and Eran Yahav. 2022. How Attentive are Graph Attention Networks?. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=F72ximsx7C1"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132973"},{"key":"e_1_3_2_2_4_1","unstructured":"Carlos Castillo Debora Donato and A. Gionis. 2007. Estimating Number of Citations Using Author Reputation. In SPIRE.  Carlos Castillo Debora Donato and A. Gionis. 2007. Estimating Number of Citations Using Author Reputation. In SPIRE."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_2_6_1","volume-title":"Personalized Pagerank Graph Attention Networks. In ICASSP 2022--2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 3578--3582","author":"Choi Julie","year":"2022","unstructured":"Julie Choi . 2022 . Personalized Pagerank Graph Attention Networks. In ICASSP 2022--2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 3578--3582 . Julie Choi. 2022. Personalized Pagerank Graph Attention Networks. In ICASSP 2022--2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 3578--3582."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2016.2521657"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1154562"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_2_10_1","unstructured":"William L. Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NIPS.  William L. Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NIPS."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Taher H Haveliwala. 2002. Topic-sensitive PageRank. In WWW.  Taher H Haveliwala. 2002. Topic-sensitive PageRank. In WWW.","DOI":"10.1145\/511446.511513"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_2_13_1","unstructured":"Ziniu Hu Yuxiao Dong Kuansan Wang and Yizhou Sun. 2020. Heterogeneous graph transformer. In WWW. 2704--2710.  Ziniu Hu Yuxiao Dong Kuansan Wang and Yizhou Sun. 2020. Heterogeneous graph transformer. In WWW. 2704--2710."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","unstructured":"Han Huang Leilei Sun Bowen Du Chuanren Liu Weifeng Lv and Hui Xiong. 2021. Representation Learning on Knowledge Graphs for Node Importance Estimation. In KDD. 646--655.  Han Huang Leilei Sun Bowen Du Chuanren Liu Weifeng Lv and Hui Xiong. 2021. Representation Learning on Knowledge Graphs for Node Importance Estimation. In KDD. 646--655.","DOI":"10.1145\/3447548.3467342"},{"key":"e_1_3_2_2_15_1","volume-title":"HINTS: Citation Time Series Prediction for New Publications via Dynamic Heterogeneous Information Network Embedding. In WWW. 3158--3167.","author":"Jiang Song","year":"2021","unstructured":"Song Jiang , Bernard Koch , and Yizhou Sun . 2021 . HINTS: Citation Time Series Prediction for New Publications via Dynamic Heterogeneous Information Network Embedding. In WWW. 3158--3167. Song Jiang, Bernard Koch, and Yizhou Sun. 2021. HINTS: Citation Time Series Prediction for New Publications via Dynamic Heterogeneous Information Network Embedding. In WWW. 3158--3167."},{"key":"e_1_3_2_2_16_1","article-title":"Representation Learning for Dynamic Graphs: A Survey","volume":"21","author":"Kazemi Seyed Mehran","year":"2020","unstructured":"Seyed Mehran Kazemi , Rishab Goel , Kshitij Jain , Ivan Kobyzev , Akshay Sethi , Peter Forsyth , Pascal Poupart , and Karsten M. Borgwardt . 2020 . Representation Learning for Dynamic Graphs: A Survey . J. Mach. Learn. Res. , Vol. 21 (2020), 70:1--70:73. Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, Pascal Poupart, and Karsten M. Borgwardt. 2020. Representation Learning for Dynamic Graphs: A Survey. J. Mach. Learn. Res., Vol. 21 (2020), 70:1--70:73.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"crossref","unstructured":"Qing Ke Emilio Ferrara Filippo Radicchi and Alessandro Flammini. 2015. Defining and identifying sleeping beauties in science. In WWW. 7426--7431.  Qing Ke Emilio Ferrara Filippo Radicchi and Alessandro Flammini. 2015. Defining and identifying sleeping beauties in science. In WWW. 7426--7431.","DOI":"10.1073\/pnas.1424329112"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052643"},{"key":"e_1_3_2_2_19_1","volume-title":"Eddy Jing Yin, and Ji-Rong Wen.","author":"Li Siqing","year":"2019","unstructured":"Siqing Li , Wayne Xin Zhao , Eddy Jing Yin, and Ji-Rong Wen. 2019 . A neural citation count prediction model based on peer review text. In EMNLP-IJCNLP. 4914--4924. Siqing Li, Wayne Xin Zhao, Eddy Jing Yin, and Ji-Rong Wen. 2019. A neural citation count prediction model based on peer review text. In EMNLP-IJCNLP. 4914--4924."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Xin Liu Junchi Yan Shuai Xiao Xiangfeng Wang Hongyuan Zha and Stephen Chu. 2017. On predictive patent valuation: Forecasting patent citations and their types. In AAAI.  Xin Liu Junchi Yan Shuai Xiao Xiangfeng Wang Hongyuan Zha and Stephen Chu. 2017. On predictive patent valuation: Forecasting patent citations and their types. In AAAI.","DOI":"10.1609\/aaai.v31i1.10722"},{"key":"e_1_3_2_2_22_1","volume-title":"Tong Zhao, and Christos Faloutsos.","author":"Park Namyong","year":"2019","unstructured":"Namyong Park , Andrey Kan , Xin Luna Dong , Tong Zhao, and Christos Faloutsos. 2019 . Estimating node importance in knowledge graphs using graph neural networks. In KDD. 596--606. Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, and Christos Faloutsos. 2019. Estimating node importance in knowledge graphs using graph neural networks. In KDD. 596--606."},{"key":"e_1_3_2_2_23_1","volume-title":"Modeling Relational Data with Graph Convolutional Networks. ArXiv","author":"Schlichtkrull M.","year":"2018","unstructured":"M. Schlichtkrull , Thomas Kipf , Peter Bloem , Rianne van den Berg , Ivan Titov , and Max Welling . 2018. Modeling Relational Data with Graph Convolutional Networks. ArXiv , Vol. abs\/ 1703 .06103 ( 2018 ). M. Schlichtkrull, Thomas Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. 2018. Modeling Relational Data with Graph Convolutional Networks. ArXiv, Vol. abs\/1703.06103 (2018)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2812203"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"crossref","unstructured":"Huawei Shen Dashun Wang Chaoming Song and Albert-L\u00e1szl\u00f3 Barab\u00e1si. 2014. Modeling and predicting popularity dynamics via reinforced poisson processes. In AAAI.  Huawei Shen Dashun Wang Chaoming Song and Albert-L\u00e1szl\u00f3 Barab\u00e1si. 2014. Modeling and predicting popularity dynamics via reinforced poisson processes. In AAAI.","DOI":"10.1609\/aaai.v28i1.8739"},{"key":"e_1_3_2_2_26_1","volume-title":"Science","volume":"354","author":"Sinatra Roberta","year":"2016","unstructured":"Roberta Sinatra , Dashun Wang , Pierre Deville , Chaoming Song , and A L Barabasi . 2016 . Quantifying the evolution of individual scientific impact . Science , Vol. 354 (2016). Roberta Sinatra, Dashun Wang, Pierre Deville, Chaoming Song, and A L Barabasi. 2016. Quantifying the evolution of individual scientific impact. Science, Vol. 354 (2016)."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","unstructured":"Mayank Singh Ajay Jaiswal Priya Shree Arindam Pal Animesh Mukherjee and Pawan Goyal. 2017. Understanding the impact of early citers on long-term scientific impact. In JCDL. 1--10.  Mayank Singh Ajay Jaiswal Priya Shree Arindam Pal Animesh Mukherjee and Pawan Goyal. 2017. Understanding the impact of early citers on long-term scientific impact. In JCDL. 1--10.","DOI":"10.1109\/JCDL.2017.7991560"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","unstructured":"Jie Tang Jing Zhang Limin Yao Juanzi Li Li Zhang and Zhong Su. 2008. Arnetminer: extraction and mining of academic social networks. In KDD. 990--998.  Jie Tang Jing Zhang Limin Yao Juanzi Li Li Zhang and Zhong Su. 2008. Arnetminer: extraction and mining of academic social networks. In KDD. 990--998.","DOI":"10.1145\/1401890.1402008"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-007-0094-2"},{"key":"e_1_3_2_2_30_1","article-title":"Visualizing data using t-SNE","volume":"9","author":"der Maaten Laurens Van","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton . 2008 . Visualizing data using t-SNE . Journal of machine learning research , Vol. 9 , 11 (2008). Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research, Vol. 9, 11 (2008).","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_2_31_1","volume-title":"Attention is All you Need. ArXiv","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam M. Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N. Gomez , \u0141ukasz Kaiser , and Illia Polosukhin . 2017. Attention is All you Need. ArXiv , Vol. abs\/ 1706 .03762 ( 2017 ). Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. ArXiv, Vol. abs\/1706.03762 (2017)."},{"key":"e_1_3_2_2_32_1","volume-title":"Science","volume":"342","author":"Wang Dashun","year":"2013","unstructured":"Dashun Wang , Chaoming Song , and Albert-L\u00e1szl\u00f3 Barab\u00e1si . 2013 . Quantifying long-term scientific impact . Science , Vol. 342 , 6154 (2013), 127--132. Dashun Wang, Chaoming Song, and Albert-L\u00e1szl\u00f3 Barab\u00e1si. 2013. Quantifying long-term scientific impact. Science, Vol. 342, 6154 (2013), 127--132."},{"key":"e_1_3_2_2_33_1","volume-title":"Yu","author":"Wang Xiao","year":"2020","unstructured":"Xiao Wang , Deyu Bo , Chuan Shi , Shaohua Fan , Yanfang Ye , and Philip S . Yu . 2020 . A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources. ArXiv , Vol. abs\/ 2011 .14867 (2020). Xiao Wang, Deyu Bo, Chuan Shi, Shaohua Fan, Yanfang Ye, and Philip S. Yu. 2020. A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources. ArXiv, Vol. abs\/2011.14867 (2020)."},{"key":"e_1_3_2_2_34_1","volume-title":"Heterogeneous Graph Attention Network. The World Wide Web Conference","author":"Wang Xiao","year":"2019","unstructured":"Xiao Wang , Houye Ji , Chuan Shi , Bai Wang , Peng Cui , Pinggang Yu , and Yanfang Ye . 2019 . Heterogeneous Graph Attention Network. The World Wide Web Conference (2019). Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, Pinggang Yu, and Yanfang Ye. 2019. Heterogeneous Graph Attention Network. The World Wide Web Conference (2019)."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_2_36_1","unstructured":"Shuai Xiao Junchi Yan Changsheng Li Bo Jin Xiangfeng Wang Xiaokang Yang Stephen M Chu and Hongyuan Zha. 2016. On Modeling and Predicting Individual Paper Citation Count over Time. In IJCAI. 2676--2682.  Shuai Xiao Junchi Yan Changsheng Li Bo Jin Xiangfeng Wang Xiaokang Yang Stephen M Chu and Hongyuan Zha. 2016. On Modeling and Predicting Individual Paper Citation Count over Time. In IJCAI. 2676--2682."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2927011"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/2232817.2232831"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"crossref","unstructured":"Rui Yan Jie Tang Xiaobing Liu Dongdong Shan and Xiaoming Li. 2011. Citation count prediction: learning to estimate future citations for literature. In CIKM. 1247--1252.  Rui Yan Jie Tang Xiaobing Liu Dongdong Shan and Xiaoming Li. 2011. Citation count prediction: learning to estimate future citations for literature. In CIKM. 1247--1252.","DOI":"10.1145\/2063576.2063757"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11192-014-1279-6"},{"key":"e_1_3_2_2_41_1","volume-title":"Modeling and predicting citation count via recurrent neural network with long short-term memory. arXiv preprint arXiv:1811.02129","author":"Yuan Sha","year":"2018","unstructured":"Sha Yuan , Jie Tang , Yu Zhang , Yifan Wang , and Tong Xiao . 2018. Modeling and predicting citation count via recurrent neural network with long short-term memory. arXiv preprint arXiv:1811.02129 ( 2018 ). Sha Yuan, Jie Tang, Yu Zhang, Yifan Wang, and Tong Xiao. 2018. Modeling and predicting citation count via recurrent neural network with long short-term memory. arXiv preprint arXiv:1811.02129 (2018)."},{"key":"e_1_3_2_2_42_1","volume-title":"Utilizing Citation Network Structure to Predict Citation Counts: A Deep Learning Approach. arXiv preprint arXiv:2009.02647","author":"Zhao Qihang","year":"2020","unstructured":"Qihang Zhao . 2020. Utilizing Citation Network Structure to Predict Citation Counts: A Deep Learning Approach. arXiv preprint arXiv:2009.02647 ( 2020 ). Qihang Zhao. 2020. Utilizing Citation Network Structure to Predict Citation Counts: A Deep Learning Approach. arXiv preprint arXiv:2009.02647 (2020)."}],"event":{"name":"CIKM '22: The 31st ACM International Conference on Information and Knowledge Management","location":"Atlanta GA USA","acronym":"CIKM '22","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557398","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557398","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:48:55Z","timestamp":1750182535000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557398"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":41,"alternative-id":["10.1145\/3511808.3557398","10.1145\/3511808"],"URL":"https:\/\/doi.org\/10.1145\/3511808.3557398","relation":{},"subject":[],"published":{"date-parts":[[2022,10,17]]},"assertion":[{"value":"2022-10-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}