Dr. Shuo Yu is currently an Associate Professor in School of Computer Science and Technology, Dalian University of Technology. She has published over 50 papers in ACM/IEEE conferences, journals, as well as magazines, and received several academic awards, including IEEE DataCom 2017 Best Paper Award, IEEE CSDE 2020 Best Paper Award, ACM/IEEE JCDL 2020 The Vannevar Bush Best Paper Honorable Mention, and CAAI BDSC2022 Rising Star. Dr. Yu received 970 citations to her work. She has served as the Track Chair of Knowledge Graph Track on ACM/SIGAPP SAC 2022 (KG2021) and Program Chair of ICDM Workshop on Knowledge Graphs (KG2022), as well as PC member of many other international conferences. Her research interests include data science, knowledge science, and graph learning. She is a lifelong member of CAAI. She is a member of ACM, IEEE, and CCF. She is also a member of IEEE Young Professionals, IEEE Computational Intelligence Society, IEEE Standards Association, and IEEE Women in Engineering.

🔥 News

  • 🎉 Our team recruits excellent doctoral students, master students, and undergraduate students all the year round. We welcome students who are committed and interested to scientific to join us!
  • 🎉 CALL FOR PAPERS in Mathematics [JCR Q1] Special Issue on Artificial Intelligence and Data Science [click to submit] extended to 1st, April, 2024.
  • 🎉 CALL FOR PAPERS in The European Physical Journal B for the special issue “New frontiers in exploring the dynamics of community structure in online social networks” (Please make sure the right special issue is selected.) [click to submit] extended to 1st, April, 2024.

Experiences

  • Shenyang University of Technology
    B.Sc. (School of Science), 2007-2011
  • Shenyang University of Technology
    M.Sc. (School of Science), 2011-2014
  • Dalian University of Technology
    Ph.D. (School of Software), 2015-2019
  • Dalian University of Technology
    Postdoc (School of Computer Science and Technology), 2019-2022
  • Dalian University of Technology
    Associate Professor (School of Computer Science and Technology), 2022-now

Research Interests

  • Data Science: Big Data, Data Mining, Domain-specific Data (e.g., Healthcare, Academia, etc.), Anomaly Detection, LLMs, Knowledge Graphs
  • Graph Learning: Responsible Graph Learning, Federated Graph Learning, Multi-modal Graph Learning, Physics-informed Graph Learning

Honors and Awards

  • The First Term SMP-IDATA Chenxing Youth Fund, National Conference of Social Media Processing and Data Space Research Institute of Hefei Comprehensive National Science Center (2023) (Funding rate <10%)
  • Most Popular Article Award, CAAI National Conference on Big Data & Social Computing (BDSC2023)
  • Distinguished Reviewer Award, CAAI National Conference on Big Data & Social Computing (BDSC2023)
  • Rising Star, ACM China Council Dalian Chapter (2022)
  • Rising Star, CAAI National Conference on Big Data & Social Computing (BDSC2022)
  • The Vannevar Bush Best Paper Honorable Mention,The ACM/IEEE Joint Conference on Digital Libraries (JCDL2020)
  • Best Paper Award, IEEE International Conference on Computer Science and Data Engineering (IEEE CSDE2020)
  • Best Paper Award, IEEE International Conference on Big Data Intelligence and Computing (IEEE DataCom2017)

Professional Affiliations

  • Editorial Board Member, Humanities and Social Sciences Communications.(https://www.nature.com/palcomms/)
  • Lead Guest Editor, Mathematics Special Issues on Artificial Intelligence and Data Science

Academic Activities

  • PC Member, SIAM International Conference on Data Mining (SDM2024)
  • PC Chairs, Data Science and Information Technology (DSIT 2024)
  • TPC Member, The 24th International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (ACM Mobihoc 2023)
  • PC Member, The 23th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2023)
  • PC Member, The 3th International Workshop on Scientific Knowledge: Representation, Discovery, and Assessment (Sci-K 2023)
  • TPC Member, The 2023 International Joint Conference on Neural Networks (IJCNN2023)
  • TPC Member, The 8th China National Conference on Big Data & Social Computing (BDSC2023)
  • Program Chair, IEEE ICDM 2022 Workshop on Knowledge Graphs (KG2022)
  • Track Chair, The 37th ACM/SIGAPP Symposium on Applied Computing (ACM/SIGAPP SAC2022) (KG2021)
  • PC Member, IEEE ICDM 2021 Workshop on Intelligence-Augmented Anomaly Analytics (IAAA2021)
  • PC Member, The 2021 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology Special Track on Social Computing

Invited Reviewer

  • ACM Transactions on Knowledge Discovery (TKDD)
  • IEEE Transactions on Emerging Topics in Computing (TETC)
  • IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
  • IEEE Transactions on Industrial Informatics (TII)
  • IEEE Intelligent Systems
  • IEEE Systems Journal
  • Artificial Intelligence Review (IF 9.588)
  • Journal of Network and Computer Applications
  • The Computer Journal
  • Journal of Computational Social Science

Fundings

  • Lead Principal Investigator, National Science Foundation Program of China (No. 62102060), Deep Graph Learning Based Group Anomaly Detection in Social Networks.
  • Lead Principal Investigator, Fundamental Research Funds for the Central Universities, Higher-order Structure-aware Group Anomaly Detection.
  • Lead Principal Investigator, SMP-IDATA Chenxing Youth Fund, Online Social Group Profiling for Online Social Networks.
  • Lead Principal Investigator, Diagnosis Assistant Platform of Nervous System Diseases Based on Multi-channel Attentional EEG Network, Dalian University.
  • Lead Principal Investigator, Advanced Science and Technology Innovation Program (No. CXY-ZKQN-2019-048), Formulation and Evolution of Academic Teams, China Association for Science and Technology.
  • Lead Principal Investigator, Unstructured Perioperative Period Data Governance, Shenzhen Comen Medical Instruments Co., Ltd.
  • Lead Principal Investigator, A Graph Representation Learning Model-Based Approach for EEG Data Classification.
  • Lead Principal Investigator, Unstructured Data Governance Based on NLP Technology.
  • Lead Principal Investigator, A Knowledge-Driven Approach to Collaborative Decision Making in Bio-Intelligent Groups.
  • Main Investigator, National Science Foundation Program of China (No. 61872054)
  • Main Investigator, Fundamental Research Funds for the Central Universities (No. DUT19LAB23)
  • Main Investigator, National Science Foundation Program of China (No. 71774020)
  • Main Investigator, Intelligent Diagnosis and Prevention Platform for Cardiovascular and Cerebrovascular Complications of Diabetes Based on Mass Spectrometry Data
  • Lead Investigator, Knowledge-Driven Collective Decision Intelligence under Resource-Constrained Condition (No. 82231024)

Conference Talks

  • MEGA:Explaining Graph Neural Networks with Network Motifs. International Joint Conference on Neural Networks (IJCNN 2023), Queensland, Australia, June 18-23, 2023
  • Deep Graph Learning: Data, Methods, and Applications. The International Workshop on the 22nd IEEE International Conference on Data Mining (MLoG 2022). (Keynote Speak)
  • Graph Augmentation Learning. The 31st International World Wide Web Conference Workshop on Graph Learning, Virtual Conference, April 25-29, 2022
  • CRI:Measuring City Infection Risk amid COVID-19. IEEE International Conference on Computer Science and Data Engineering 2020 (IEEE CSDE 2020), Virtual Event, Australia, December 16-18, 2020 (Best Paper Award)
  • OFFER: A Motif Dimensional Framework for Network Representation Learning. The 29th ACM International Conference On Information And Knowledge Management (CIKM2020), Virtual Event, October 19-23, 2020
  • Multivariate Relations Aggregation Learning in Social Networks. Proceedings of the ACM/IEEE Joint Conference on Digital Libraries (JCDL 2020), Virtual Event, China, August 1-5, 2020 (The Vannevar Bush Best Paper Honorable Mention)
  • Mining Key Scholars via Collapsed Core and Truss. The 4th IEEE International Conference on Cyber Science and Technology Congress, Fukuoka, Japan, August 7, 2019
  • Team Recognition in Big Scholarly Data: Exploring Collaboration Intensity. The 3rd IEEE International Conference on Big Data Intelligence and Computing (DataCom), Orlando, Florida, USA, November 6, 2017 (Best Paper Award)
  • A Modified Node2vec Method for Disappearing Link Prediction. The 3rd IEEE International Conference on Big Data Intelligence and Computing (DataCom), Orlando, Florida, USA, November 6, 2017
  • CAR: Incorporating Filtered Citation Relations for Scientific Article Recommendation. The 8th IEEE International Conference on Social Computing and Networking (SocialCom), Chengdu, China, December 20, 2015

Selected Publications

  • Tao Tang, Zhuoyang Han, Zhen Cai, Shuo Yu*, Xiaokang Zhou, Taiwo Oseni, and Sajaland Das. 2024. Personalized Federated Graph Learning on Non-llD Electronic Health Records. IEEE Transactions on Neural Networks and Learning Systems. (Accepted)
  • Ciyuan Peng, Mujie Liu, Chenxuan Meng, Shuo Yu*, and Feng Xia. 2024. Adaptive Brain Network Augmentation based on Group-aware Graph Learning. International Conference on Learning Representations (ICLR2024). (Accepted)
  • Shuo Yu, Huafei Huang, Yanming Shen, Pengfei Wang, Qiang Zhang, Ke Sun, Honglong Chen. 2024. Formulating and Representing Multi-agent Systems with Hypergraphs. IEEE Transactions on Neural Networks and Learning Systems. (Accepted)
  • Zhaoxian Dong, Shuo Yu*, and Yanming Shen. 2024. Multi-scale Dynamic Hypergraph Convolution Network for Traffic Flow Forecasting. Journal of Shanghai Jiao Tong University (Science). (Accepted)
  • Yuting Ma, Shuo Yu*, and Yanming Shen. 2024. Pretraining Molecules with Explicit Substructure Information. SIAM International Conference on Data Mining (SIAM-SDM24). (Accepted)
  • Shan Jin, Zhikui Chen, Shuo Yu*, Muhammad Altaf, and Zhenchao Ma. 2023. Self-Augmentation Graph Contrastive Learning for Multi-view Attribute Graph Clustering. In proceedings of the 2023 Workshop on Advanced Multimedia Computing for Smart Manufacturing and Engineering. Association for Computing Machinery, New York, NY, USA, 51–56. https://doi.org/10.1145/3606042.3616455.
  • Mourad Lablack, Shuo Yu*, Shuai Xu, Yanming Shen. Long-sequence model for traffic forecasting in suboptimal situation. 2023. Proceedings of the 18th Workshop on Mobility in the Evolving Internet Architecture (MobiArch'23). Association for Computing Machinery, New York, NY, USA, 25–30. https://doi.org/10.1145/3615587.3615985
  • Shuo Yu, Ciyuan Peng, Yingbo Wang, Ahsan Shehzad, Feng Xia, and Edwin R. Hancock. 2023. Quantum Graph Learning: Frontiers and Outlook. arXiv preprint arXiv:2302.00892. (PDF)
  • Feng Ding, Naiwen Luo, Shuo Yu*, and Zhikui Chen. 2023. MEGA: Explaining Graph Neural Networks with Network Motifs. 2023 International Joint Conference on Neural Networks (IJCNN). Gold Coast, Australia, 1-9. doi: 10.1109/IJCNN54540.2023.10191684
  • Shuo Yu, Feng Xia, Shihao Li, Mingliang Hou, and Quan Z. Sheng. 2023. Spatio-Temporal Graph Learning for Epidemic Prediction. ACM Transactions on Intelligent Systems and Technology 14, 2, 1-25. (PDF) (CODE)
  • Shuo Yu, Ciyuan Peng, Chengchuan Xu, Chen Zhang, and Feng Xia. 2023. Web of Conferences: A Conference Knowledge Graph. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM). 1172-1175, DOI:10.1145/3539597.3573024. (PDF)
  • Shuo Yu, Feng Xia, Yueru Wang, Shihao Li, Falih Gozi Febrinanto, and Madhu Chetty. 2022. PANDORA: Deep Graph Learning Based COVID-19 Infection Risk Level Forecasting. IEEE Transactions on Computational Social Systems. 1-14. DOI: 10.1109/TCSS.2022.3229671. (PDF)
  • Xu Yuan, Ying Yang, Huafei Huang, Shuo Yu*, and Lili Cong. 2022. Mining Implicit Relations Among Image Channels for Few-Shot Semantic Segmentation. The 19th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC). 275-284, doi: 10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00062.
  • Shuo Yu, Jing Ren, Shihao Li, Mehdi Naseriparsa, and Feng Xia. 2022. Graph Learning for Fake Review Detection. Frontiers in Artificial Intelligence 5. (PDF)
  • Xu Yuan, Qihang Lei, Shuo Yu, Chengchuan Xu, and Zhikui Chen. Fine-grained relational learning for few-shot knowledge graph completion. ACM SIGAPP Applied Computing Review, Rev. 22, 3 (September 2022), 25–38. (PDF)
  • Shuo Yu, Chengchuan Xu, Xiaomei Bai, Ranjith Kuncheerathodi, Selena Firmin, and Feng Xia. Deep Learning Meets Knowledge Graphs: A Comprehensive Survey. 06 September 2022, PREPRINT (Version 1), DOI: 10.21203/rs.3.rs-2021923/v1 (PDF)
  • Shuo Yu, Sihan He, Zhen Cai, Ivan Lee, Mehdi Naseriparsa, and Feng Xia. 2022. Exploring Public Sentiment During COVID-19: A Cross Country Analysis. IEEE Transactions on Computational Social Systems 10, 3 (2023), 1083–1094. (PDF)
  • Hayat D. Bedru, Chen Zhang, Feng Xie, Shuo Yu, and Iftikhar Hussain. 2023. CLARA: citation and similarity-based author ranking. Scientometrics 128, 1091–1117. DOI: 10.1007/s11192-022-04590-5 (PDF)
  • Zhikui Chen, Yin Peng, Shuo Yu*, Chen Cao, and Feng Xia. 2022. Subgraph Adaptive Structure-Aware Graph Contrastive Learning. Mathematics 10, 17, 3047. DOI: 10.3390/math10173047 (PDF) (CODE)
  • Lei Wang, Shuo Yu*, Falih Gozi Febrinanto, Fayez Alqahtani, and Tarek E. El-Tobely. Fairness-Aware Predictive Graph Learning in Social Networks. Mathematics 10, 15, 2696. DOI: 10.3390/math10152696 (PDF)
  • Ke Sun, Shuo Yu*, Ciyuan Peng, Yueru Wang, Osama Alfarraj, Amr Tolba, and Feng Xia. 2022. Relational Structure-Aware Knowledge Graph Representation in Complex Space. Mathematics 10, 11, 1930. DOI: 10.3390/math10111930 (PDF)
  • Shuo Yu, Feng Xia, Chen Zhang, Haoran Wei, Kathleen Keogh, and Honglong Chen. 2022. Familiarity-based Collaborative Team Recognition in Academic Social Networks. IEEE Transactions on Computational Social Systems 9, 5, 1432–1445. DOI: 10.1109/TCSS.2021.3129054 (PDF)
  • Qi Lin, Shuo Yu, Ke Sun, Wenhong Zhao, Osama Alfarraj, Amr Tolba, and Feng Xia. 2022. Robust Graph Neural Networks via Ensemble Learning. Mathematics 10, 8, 1300. DOI: 10.3390/math10081300 (PDF)
  • Shuo Yu, Huafei Huang, Minh N. Dao, and Feng Xia. 2022. Graph Augmentation Learning. 2022. In Companion Proceedings of the Web Conference 2022 (Virtual Event, Lyon, France) (WWW ’22). Association for Computing Machinery, New York, NY, USA, 1063–1072. DOI: 10.1145/3487553.3524718 (PDF) (CODE)
  • Feng Xia, Shuo Yu*, Chengfei Liu, Jianxin Li, and Ivan Lee. 2022. CHIEF: Clustering with Higher order Motifs in Big Networks. IEEE Transactions on Network Science and Engineering 9, 3, 990–1005. DOI: 10.1109/TNSE.2021.3108974 (PDF) (CODE)
  • Feng Xia, Ke Sun, Shuo Yu, Aziz, Abdul, Liangtian Wan, Shirui Pan, and Huan Liu. 2021. Graph Learning: A Survey. IEEE Transactions on Artificial Intelligence 2, 2, 109–127. DOI: 10.1109/TAI.2021.3076021 (PDF)
  • Xu Yuan, Na Zhou, Shuo Yu, Huafei Huang, Zhikui Chen, and Feng Xia. 2021. Higher-order Structure Based Anomaly Detection on Attributed Networks. In 2021 IEEE International Conference on Big Data (IEEE BigData 2021). 2691–2700. (PDF) (CODE)
  • Nakema Y. Deonauth, Mingchu Li, Shuo Yu, and Xiangtai Chen. 2021. An Upstream-Reciprocity-Based Strategy for Academic Social Networks Using Public Goods Game. IEEE Transactions on Computational Social Systems 8, 6, 1417-1426. DOI: 10.1109/TCSS.2021.3085174 (PDF)
  • Shuo Yu, Qing Qing, Chen Zhang, Ahsan Shehzad, Giles Oatley, and Feng Xia. 2021. Data-Driven Decision-Making in COVID-19 Response: A Survey. IEEE Transactions on Computational Social Systems, 8, 1016-1029. DOI: 10.1109/TCSS.2021.3075955 (PDF)
  • Chen Cao, Shihao Li, Shuo Yu*, and Zhikui Chen. 2021. Fake Reviewer Group Detection in Online Review Systems. In 2021 International Conference on Data Mining Workshops (ICDMW). 935–942. (PDF) (CODE)
  • Shuo Yu, Hayat Dino Bedru, Xinbei Chu, Yuyuan Yuan, Liangtian Wan, and Feng Xia. 2020. Understanding Serendipity in Science: A Survey. Data Analysis and Knowledge Discovery, 5, 1, 16-35. DOI: 10.11925/infotech.2096-3467.2020.1088 (PDF)
  • Shuo Yu, Jiaying Liu, Feng Xia, and Haoran Wei, Hanghang Tong. 2020. How to optimize an academic team when the outlier member is leaving? IEEE Intelligent Systems, 36, 3, 23-30. DOI: 10.1109/MIS.2020.3042871 (PDF)
  • Shuo Yu, Feng Xia, Jin Xu, Zhikui Chen, and Ivan Lee. 2020. OFFER: A Motif Dimensional Framework for Network Representation Learning. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (Virtual Event, Ireland) (CIKM ’20).. Association for Computing Machinery, New York, NY, USA, 3349–3352. (PDF)
  • Shuo Yu, Feng Xia, Yuchen Sun, Tao Tang, Xiaoran Yan, and Ivan Lee. 2020. Detecting Outlier Patterns with Query-based Artificially Generated Searching Conditions, IEEE Transactions on Computational Social Systems, 8, 134-147. DOI: 10.1109/TCSS.2020.2977958 (PDF)
  • Mingliang Liu, Shuo Yu*, Xinbei Chu, and Feng Xia. 2020. CRI: Measuring City Infection Risk amid COVID-19. In 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE). 1–6. (Best Paper Award) (PDF)
  • Jin Xu, Shuo Yu, Ke Sun, Jing Ren, Ivan Lee, Shirui Pan, and Feng Xia. 2020. Multivariate Relations Aggregation Learning in Social Networks. In Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020 (Virtual Event, China) (JCDL ’20).. Association for Computing Machinery, New York, NY, USA, 77–86. (The Vannevar Bush Best Paper Honorable Mention) (PDF)
  • Hayat Dino, Shuo Yu, Liangtian Wan, Mengyang Wang, Kaiyuan Zhang, He Guo, and Iftikhar Hussain. 2020. Detecting leaders and key members of scientific teams in co-authorship networks, Computers Electrical Engineering, 85, 106703. DOI: 10.1016/j.compeleceng.2020.106703 (PDF)
  • Hayat Dino Bedru, Shuo Yu, Xinru Xiao, Da Zhang, Liangtian Wan, He Guo, and Feng Xia. 2020. Big networks: A survey. Computer Science Review, 37, 100247. DOI: 10.1016/j.cosrev.2020.100247 (PDF)
  • Shuo Yu, Yufan Feng, Da Zhang, Hayat Dino Bedru, Bo Xu, and Feng Xia. 2020. Motif discovery in networks: A survey. Computer Science Review, 37, 100267. DOI: 10.1016/j.cosrev.2020.100267 (PDF)
  • Ke Sun, Jiaying Liu, Shuo Yu, Bo Xu, and Feng Xia. 2020. Graph Force Learning. In 2020 IEEE International Conference on Big Data (Big Data).. 2987–2994 (PDF) (CODE)
  • Shuo Yu, Yuanhu Liu, Jing Ren, Hayat Dino Bedru, Teshome Megersa Bekele, Liangtian Wan, and Feng Xia. 2019. Mining Key Scholars via Collapsed Core and Truss. The 4th Cyber Science and Technology Congress (CyberSciTech), 305-308. (PDF)
  • Shuo Yu, Hayat Dino Bedru, Ivan Lee, and Feng Xia. 2019. Science of Scientific Team Science: A Survey. Computer Science Review, 31, 72-83. DOI: 10.1016/j.cosrev.2018.12.001 (PDF)
  • Yufan Feng, Shuo Yu, Kaiyuan Zhang, Xiangli Li, and Zhaolong Ning. 2019. COMICS: a community property-based triangle motif clustering scheme. PeerJ Computer Science, 5, e180. DOI 10.7717/peerj-cs.180 (PDF) (CODE)
  • Shuo Yu, Feng Xia, and Huan Liu. 2019. Academic Team Formulation Based on Liebig’s Barrel: Discovery of Anti Cask Effect. IEEE Transactions on Computational Social Systems, 6, 1083-1094. DOI: 10.1109/TCSS.2019.2913460 (PDF)
  • Kaiyuan Zhang, Shuo Yu, Liangtian Wan, Jianxin Li, and Feng Xia. 2019. Predictive Representation Learning in Motif-based Graph Networks. The 32nd Australasian Joint Conference on Artificial Intelligence (AI2019),177–188. (PDF)
  • Bo Xu, Yu Liu, Shuo Yu*, Lei Wang, Jie Dong, Hongfei Lin, Zhihao Yang, Jian Wang and Feng Xia. 2019. A Network Embedding Model for Pathogenic Genes Prediction by Multi-path Random Walking on Heterogeneous Network. BMC Medical Genomics, 12, 1-12. DOI:10.1186/s12920-019-0627-z (PDF)
  • Xiangjie Kong, Yajie Shi, Shuo Yu, Jiaying Liu, and Feng Xia. 2019. Academic Social Networks: Modeling, Analysis, Mining and Applications, Journal of Network and Computer Applications, 132, 86-103. DOI: 10.1016/j.jnca.2019.01.029 (PDF)
  • Xiangjie Kong, Lei Liu, Shuo Yu, Andong Yang, Xiaomei Bai, and Bo Xu. 2019. Skill ranking of researchers via hypergraph. PeerJ Computer Science 5, e182. DOI 10.7717/peerj-cs.182 (PDF)
  • Wei Wang, Bo Xu, Jiaying Liu, Zixin Cui, Shuo Yu, Xiangjie Kong, and Feng Xia. 2019. CSTeller: forecasting scientific collaboration sustainability based on extreme gradient boosting. World Wide Web 22, 6 (01 Nov 2019), 2749–2770. DOI: 10.1007/s11280-019-00703-y (PDF)
  • Xiangjie Kong, Mengyi Mao, Huizhen Jiang, Shuo Yu, and Liangtian Wan. 2019. How does Collaboration Affect Researchers’ Positions in Co-authorship Networks? Journal of Informetrics, 13, 3 (Aug. 2019), 887–900. DOI: 10.1016/j.joi.2019.07.005 (PDF)
  • Shuo Yu, Jiaying Liu, Zhuo Yang, Zhen Chen, Huizhen Jiang, Amr Tolba, and Feng Xia. 2018. PAVE: Personalized Academic Venue recommendation Exploiting co-publication networks. Journal of Network and Computer Applications, 104, 38–47. DOI: 10.1016/j.jnca.2017.12.004 (PDF)
  • Bo Xu, Yu Liu, Shuo Yu, Lei Wang, Lei Liu, Hongfei Lin, Zhihao Yang, Jian Wang, and Feng Xia. 2018. Multipath2vec: Predicting Pathogenic Genes via Heterogeneous Network Embedding. In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 951–956. (PDF)
  • Wei Wang, Shuo Yu, Teshome Megersa Bekele, Xiangjie Kong, and Feng Xia. 2017. Scientific collaboration patterns vary with scholars’ academic ages. Scientometrics 112, 1, 329–343. DOI: 10.1007/s11192-017-2388-9. (PDF)
  • Li Liu, Shuo Yu, Xiang Wei, and Zhaolong Ning. 2018. An improved Apriori–based algorithm for friends recommendation in microblog. International Journal of Communication Systems 31, 2, e3453. (PDF)
  • Lu Li, Wei Wang, Shuo Yu, Liangtian Wan, Zhenzhen Xu, and Xiangjie Kong. 2017. Detection of four-node motif in complex networks. In Complex Networks & Their Applications VI, Chantal Cherifi, Hocine Cherifi, Márton Karsai, and Mirco Musolesi (Eds.). Springer International Publishing, Cham, 453–462. (PDF)
  • Wei Wang, Zixin Cui, Tong Gao, Shuo Yu, Xiangjie Kong, and Feng Xia. 2016. Is Scientific Collaboration Sustainability Predictable? In Proceedings of the 26th International Conference on World Wide Web Companion (WWW’17 Companion). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 853–854 (PDF)
  • Lu Li, Wei Wang, Shuo Yu, Liangtian Wan, Zhenzhen Xu, and Xiangjie Kong. 2017. A Modified Node2vec Method for Disappearing Link Prediction. In 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech). 1232–123. (PDF)
  • Shuo Yu, Feng Xia, Kaiyuan Zhang, Zhaolong Ning, Jiaofei Zhong, and Chengfei Liu. 2017. Team recognition in big scholarly data: Exploring collaboration intensity. In 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech). 925–932. https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2017.155 (PDF)
  • Wei Wang, Jiaying Liu, Shuo Yu, Chenxin Zhang, Zhenzhen Xu, and Feng Xia. 2016. Mining advisor-advisee relationships in scholarly big data: A deep learning approach. In Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digtal Libraries (JCDL ’16). Association for Computing Machinery, New York, NY, USA, 209–210. (PDF)
  • Xiaoyan Su, Wei Wang, Shuo Yu, Chenxin Zhang, Teshome Megersa Bekele, and Feng Xia. 2016. Can academic conferences promote research collaboration? In Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries (JCDL ’16). Association for Computing Machinery, New York, NY, USA, 231–232. (PDF)
  • Jun Zhang, Feng Xia, Wei Wang, Xiaomei Bai, Shuo Yu, Teshome Megersa Bekele, and Zhong Peng. 2016. CocaRank: A Collaboration Caliber-based Method for Finding Academic Rising Stars. In Proceedings of the 25th International Conference Companion on World Wide Web (WWW ’16 Companion). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 395–400. (PDF)
  • Jun Zhang, Zhaolong Ning, Xiaomei Bai, and Wei Wang, Shuo Yu, and Feng Xia. 2016. Who are the Rising Stars in Academia? In Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries (JCDL ’16). Association for Computing Machinery, New York, NY, USA, 211–212. (PDF)
  • Yan Hu, Jun Zhang, Xiaomei Bai, Shuo Yu, and Zhuo Yang. 2016. Influence analysis of Github repositories. SpringerPlus. 5, 1, 1268. (PDF)
  • Haifeng Liu, Zhuo Yang, Ivan Lee, Zhenzhen Xu, Shuo Yu, and Feng Xia. 2015. CAR: Incorporating Filtered Citation Relations for Scientific Article Recommendation. In 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity). 513–518. (PDF)