Computer Science ›› 2019, Vol. 46 ›› Issue (2): 315-320.doi: 10.11896/j.issn.1002-137X.2019.02.048

• Interdiscipline & Frontier • Previous Articles     Next Articles

Social Team Formation Method Based on Fuzzy Multi-objective Evolution

JIN Ting1, TAN Wen-an1,2, SUN Yong1, ZHAO Yao1   

  1. School of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China1
    School of Computer and Information Engineering,Shanghai Polytechnic University,Shanghai 201029,China2
  • Received:2017-12-07 Online:2019-02-25 Published:2019-02-25

Abstract: The present team formation researches in social network mostly take 0-1 rule to measure expert skills.Aiming at the situation that people often utilize the natural language to describe expert skills,this paper proposed a social team formation method based on fuzzy multi-objective evolution.This method focuses on how to find out the appropriate individuals from the expert social network to form a team with certain size and achieves the optimization between communication cost and team performance under the uncertainty circumstances.In this method,the precise parameters represented by 0-1 rule are replaced by fuzzy language variables to describe expert skill.The concept of team performance is used to measure team capability.Because the standard SPEA2 algorithm has slow convergence at the initialevolutio-nary stage,this paper introduced AEL strategy to generate individuals with good characteristics.Considering the ambi-guity of expert skills,this paper also proposed a fine-grained Dominance judgment as the new rule of judging the dominance relationship of individuals.The simulation results show that the improved algorithm converges fast and obtains good quality approximate PF,which can be successfully applied to solve the team formation problem.

Key words: Evolutionary algorithm, Fuzzy language variables, Social network, Team formation

CLC Number: 

  • TP311
[1]LAPPAS T,LIU K,TERZI E.Finding a team of experts in social networks[C]∥Proceedings of the 15th ACM SIGKDD International Conf. on Knowledge Discovery and Data Mining.New York:ACM,2009:467-476.
[2]LI C T,SHAN M K.Team formation for generalized tasks in expertise social networks[C]∥Proceedings of IEEE International Conference on Social Computing.Piscataway,NJ:IEEE,2010:9-16.
[3]KARGAR M,AN A.Discovering top-k teams of experts with/without a leader in social networks[C]∥Proceedings of the 20th ACM International Conference on Information and Knowledge Management.New York:ACM,2011:985-994.
[4]KARGAR M,ZIHAYAT M,AN A.Finding affordable and collaborative teams from a network of experts[C]∥Proceedings of the 13th SIAM International Conference on Data Mining.2013:587-595.
[5]KARGAR M,AN A,ZIHAYAT M.Efficient Bi-objective Team Formation in Social Networks[C]∥Proceedings of 2012 European Conference on Machine Learning & Knowledge Discovery in Databases.Berlin:Springer-Verlag,2012:483-498.
[6]SUN Y,TAN W A,LI L,et al.A new method to identify colla- borative partners in social service provider networks[J].Information Systems Frontiers,2016,18(3):565-578.
[7]SUN Y,TAN W A.Cross-Organizational Workflow Task Allocation Algorithms for Socially Aware Collaborative Computing[J].Journal of Computer Research and Development,2017,54(9):1865-1879.(in Chinese)
[8]SUN H L,JIN M Y,LIU J L,et al.Methods for Team Formation Problem with Grouping Task in Social Networks[J].Journal of Computer Research and Development,2015,52(11):2535-2544.(in Chinese)
[9]FARHADI F,SORKHI M,HASHEMI S,et al.An effective expert team formation in social networks based on skill grading[C]∥Proceedings of 2011 IEEE 11th International Conference on Data Mining Workshops (ICDMW).New York:IEEE,2011:366-372.
[10]SUN H L,FU S S,LIU J L, Formation with Weak Ties in Social Networks[J].Journal of Frontiers of Computer Science and Technology,2016,10(6):773-785.(in Chinese)
[11]XIE C W,WANG Z J,XIA X W.Multi-Objective Evolutionary Algorithm Based on Archive-Elite Learning and Opposition-Based Learning[J].Chinese Journal of Computers,2017,40(3):757-772.(in Chinese)
[12]BAYKASOGLU A,DERELI T,DAS S.Project team selection using fuzzy optimization approach[J].Cybernetics and Systems,2007,38(2):155-185.
[1] WANG Jian, PENG Yu-qi, ZHAO Yu-fei, YANG Jian. Survey of Social Network Public Opinion Information Extraction Based on Deep Learning [J]. Computer Science, 2022, 49(8): 279-293.
[2] SUN Gang, WU Jiang-jiang, CHEN Hao, LI Jun, XU Shi-yuan. Hidden Preference-based Multi-objective Evolutionary Algorithm Based on Chebyshev Distance [J]. Computer Science, 2022, 49(6): 297-304.
[3] WEI Peng, MA Yu-liang, YUAN Ye, WU An-biao. Study on Temporal Influence Maximization Driven by User Behavior [J]. Computer Science, 2022, 49(6): 119-126.
[4] YU Ai-xin, FENG Xiu-fang, SUN Jing-yu. Social Trust Recommendation Algorithm Combining Item Similarity [J]. Computer Science, 2022, 49(5): 144-151.
[5] CHANG Ya-wen, YANG Bo, GAO Yue-lin, HUANG Jing-yun. Modeling and Analysis of WeChat Official Account Information Dissemination Based on SEIR [J]. Computer Science, 2022, 49(4): 56-66.
[6] ZUO Yuan-lin, GONG Yue-jiao, CHEN Wei-neng. Budget-aware Influence Maximization in Social Networks [J]. Computer Science, 2022, 49(4): 100-109.
[7] GUO Lei, MA Ting-huai. Friend Closeness Based User Matching [J]. Computer Science, 2022, 49(3): 113-120.
[8] SHAO Yu, CHEN Ling, LIU Wei. Maximum Likelihood-based Method for Locating Source of Negative Influence Spreading Under Independent Cascade Model [J]. Computer Science, 2022, 49(2): 204-215.
[9] WANG Jian, WANG Yu-cui, HUANG Meng-jie. False Information in Social Networks:Definition,Detection and Control [J]. Computer Science, 2021, 48(8): 263-277.
[10] TAN Qi, ZHANG Feng-li, WANG Ting, WANG Rui-jin, ZHOU Shi-jie. Social Network User Influence Evaluation Algorithm Integrating Structure Centrality [J]. Computer Science, 2021, 48(7): 124-129.
[11] ZHANG Ren-zhi, ZHU Yan. Malicious User Detection Method for Social Network Based on Active Learning [J]. Computer Science, 2021, 48(6): 332-337.
[12] BAO Zhi-qiang, CHEN Wei-dong. Rumor Source Detection in Social Networks via Maximum-a-Posteriori Estimation [J]. Computer Science, 2021, 48(4): 243-248.
[13] ZHANG Shao-jie, LU Xu-dong, GUO Wei, WANG Shi-peng, HE Wei. Prevention of Dishonest Behavior in Supply-Demand Matching [J]. Computer Science, 2021, 48(4): 303-308.
[14] LI Li, LI Guang-peng, CHANG Liang, GU Tian-long. Survey of Constrained Evolutionary Algorithms and Their Applications [J]. Computer Science, 2021, 48(4): 1-13.
[15] ZHANG Hao-chen, CAI Ying, XIA Hong-ke. Delivery Probability Based Routing Algorithm for Vehicular Social Network [J]. Computer Science, 2021, 48(3): 289-294.
Full text



No Suggested Reading articles found!