以下成果（截止到2013年10月1日）系“周涛、吕琳媛、张子柯、陈端兵、尚明生”为核心成员，合作完成。

专著数量1本，论文总量94篇，其中SCI论文76篇，Google scholar总引用3635，SCI论文总影响因子191.289, 授权专利一项，申请专利7项

专利:

（1）       刘臻, 吕琳媛, 肖思源, 刘润然,佘莉. 一种基于无线网络的数据业务推送系统和方法，申请号:201310168218.3, 申请日：2013-05-06

（2）       刘臻, 吕琳媛, 肖思源, 刘润然,佘莉.一种时间窗口的调节方法，申请号:201310169234.4, 申请日：2013-05-06

（3）       刘臻, 吕琳媛, 肖思源, 刘润然,佘莉. 根据浏览网页确定用户感兴趣的网页文本的方法和系统，申请号:201310163619.X, 申请日：2013-05-06

（4）       刘臻，吕琳媛，肖思源，刘润然，佘莉. 根据相关网页和当前行为确定用户当前兴趣的方法和系统，申请号:201310162870.4, 申请日：2013-05-06

（5）       吕琳媛,周艳波. 一种网络商品个性化推荐方法及系统，申请号:201310310951.4, 申请日：2013-07-22

（6）       尚明生,傅彦,邵刚,一种信息推送方法与装置,授权号：2007100874138，授权日：2012-10-17

（7）       尚明生,佘莉,周涛,陈端兵,傅彦,田军伟,一种用户兴趣模型的建立方法,申请号：2009101676383,申请日：2009-09-15

（8）       王庆先,尚明生. 一种向客户推荐商品的方法, 申请号：2011104483695,申请日：2011-12-28

专著：

吕琳媛，周涛，链路预测，高等教育出版社，2013

论文：

[1]     Q. Ou, Ying-DiJin, T. Zhou, B. –H. Wang, and B. –Q. Yin, Power-law strength-degree correlation fromresource-allocation dynamics on weighted networks, Phys. Rev. E 75, 021102 (2007).

[2]     T. Zhou, J. Ren, M. Medo, and Y. –C. Zhang, Bipartite network projection andpersonal recommendation,Phys. Rev.E 76, 046115 (2007).

[3]     Y. –C. Zhang,M. Medo, J. Ren, T. Zhou, T. Li, and F. Yang, Recommendation model based on opinion diffusion,EPL 80, 68003 (2007).

[4]     T. Zhou, L.-L. Jiang, R.-Q. Su, and Y.-C. Zhang, Effect of initial configurationon network-based recommendation, EPL 81,58004 (2008).

[5]     J. Ren, T. Zhou, and Y.-C. Zhang, InformationFiltering via Self-Consistent Refinement, EPL 82, 58007 (2008). 

[6]     H.-T. Zhang, M.Z. Q. Chen, G.-B. Stan, T. Zhou, and J. Maciejowski, Collective behavior coordinating with predictivemechanisms, IEEE Circuits andSystems Magazine 2008(3): 67-85 (Feature Article).

[7]      汪秉宏，周涛，王文旭，杨会杰，刘建国，赵明，殷传洋，韩筱璞，谢彦波，“当前复杂系统研究的几个方向”，复杂系统与复杂性科学 5(4): 21-28 (2008).

[8]     R.-R. Liu, C.-X.Jia, T. Zhou, D. Sun, and B.-H. Wang, Personal Recommendation via ModifiedCollaborative Filtering, PhysicaA 388: 462-468 (2009).

[9]     刘建国，周涛，汪秉宏，个性化推荐系统的研究进展， 自然科学进展 19(1):1-15 (2009).

[10] T. Zhou, Personal Recommendation in User-Object Networks, Lecture Notes of the Institute for ComputerSciences, Social-Informatics and Telecommunications Engineering 4,247-253 (2009).

[11] J.-G. Liu, M. Z. Q.Chen, J. Chen, F. Deng, H.-T. Zhang, Z.-K.Zhang, and T. Zhou, Recent Advances inPersonal Recommender Systems, InternationalJournal of Information and Systems Sciences 5, 230-247(2009).

[12] D. Sun, T. Zhou, J.-G. Liu, R.-R.Liu, C.-X. Jia, and B.-H. Wang, Information filtering based on transferringsimilarity, Phys. Rev. E 80,017101 (2009).

[13]  H.-X. Yang,Z.-X. Wu, C.-S. Zhou, T. Zhou, and B.-H. Wang, Effects of social diversity on the emergence of globalconsensus in opinion dynamics, Phys.Rev. E 80, 046108 (2009). 

[14]  L. Lü, C.-H. Jin, and T. Zhou, Effective andEfficient Similarity Index for Link Prediction of Complex Networks, Phys. Rev. E 80, 046122(2009).

[15]  M. Medo, Y.-C.Zhang, and T. Zhou, Adaptive model for recommendation of news, EPL 88, 38005 (2009).

[16] T. Zhou, R.-Q. Su, R.-R. Liu, L.-L. Jiang, B.-H. Wang, and Y.-C. Zhang, Accurateand diverse recommendations via eliminating redundant correlations, New J.Phys. 11, 123008 (2009).

[17] J.-G. Liu, T. Zhou, B.-H. Wang, Y.-C.Zhang, Q. Guo, Effects of User’s Tastes on Personalized Recommendation, Int. J. Mod. Phys. C 20,1925 (2009).

[18] 刘建国，周涛，郭强，汪秉宏，个性化推荐系统评价方法综述，复杂系统与复杂性科学，6, 1-10(2009)

[19]  T. Zhou, L. Lü,and Y.-C. Zhang, Predicting Missing Links via Local Information, Eur. Phys. J.B 71, 623-630 (2009).

[20]  M.-S. Shang, and Z.-K.Zhang, Diffusion-Based Recommendation in collaborative Tagging Systems.Chinese Physics Letters 26,118903(2009)

[21]  M.-S. Shang, L.Lü, W. Zeng, Y.-C. Zhang, andT. Zhou, Relevance is More Significant than Correlation: InformationFiltering on Sparse Data, EPL 88,68008 (2009).

[22]  L. Lü, and T. Zhou,Role of Weak Ties in Link Prediction of Complex Networks, In the proceeding ofthe 18th ACM Conference on Information and Knowledge Management (ACM, New York,2009).  

[23]  L. Lü, C.-H. Jin, T.Zhou, Similarity index based on local paths for link prediction ofcomplex network, Phys. Rev. E 80,046122 (2009).

[24]  M.-S. Shang, C.-H. Jin, T. Zhou, and Y.-C. Zhang, Collaborative filtering based onmulti-channel diffusion, PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 388, 4867-4871(2009)

[25]  L. Lü, and T. Zhou,Link Prediction in Weighted Networks: The Role of Weak Ties, EPL 89, 18001 (2010).

[26]  L. Lü, Z.-K. Zhang,and T. Zhou, Zipf’s Law Leadsto Heaps’ Law: Analyzing Their Relation in Finite-Size Systems, PLoS ONE 5(12), e14139 (2010).

[27]  M.-S. Shang, L.Lü, Y.-C. Zhang, and T. Zhou,Empirical analysis of web-based user-object bipartite networks, EPL 90, 48006 (2010).

[28]  W. Zeng, M.-S.Shang, Q.-M. Zhang, L. Lü,and T. Zhou, Can DissimilarUsers Contribute to Accuracy and Diversity of Personalized Recommendation? Int.J. Mod. Phys. C 21, 1217 (2010).

[29]  L. Lü, J.-A. Lu, Z.-K.Zhang, X.-Y. Yan, Y. Wu, D.-H. Shi, H.-P. Zhou, J.-Q. Fang, and T. Zhou, Looking into ComplexNetworks, Complex Systems and Complexity Science 7(2-3), 173 (2010).

[30]  J.-G.Liu, T. Zhou, H.-A. Che, B.-H. Wang, and Y.-C. Zhang, Effects of high-ordercorrelations on personalized recommendations for bipartite networks, Physica A 389 881(2010). 

[31] M.-S. Shang, G.-X. Chen, S.-X. Dai, B.-H. Wang, and T. Zhou, Interest-Driven Model for Human Dynamics, Chin. Phys. Lett. 27, 048701 (2010).

[32] J.-G. Liu, T. Zhou, B.-H. Wang, Y.-C.Zhang, and Q. Guo, Degree Correlation of Bipartite Network on PersonalizedRecommendation, Int. J. Mod.Phys. C 21, 137-147 (2010).

[33]  T. Zhou, Z. Kuscsik, J.-G.Liu, M. Medo, J. R. Wakeling, and Y.-C. Zhang, Solving the apparent diversity-accuracydilemma of recommender systems, PNAS 107,4511-4515 (2010) 

[34]  H.-T. Zhang, N.Wang, M. Z. Q. Chen, R.-Q. Su, T. Zhou, and C. Zhou, Spatially quantifying the leadership effectiveness incollective behaviors, New J.Phys. 12, 123025 (2010).

[35] 汪秉宏，周涛，刘建国，推荐系统、信息挖掘及基于互联网的信息物理研究，复杂系统与复杂性科学，7, 46-49(2010)

[36]  W.-P. Liu, and L. Lü, Link Prediction based on Local Random walk, EPL 89, 58007 (2010).  

[37]  M.-S. Shang, Z.-K.Zhang, T. Zhou, and Y.-C.Zhang, Collaborative filtering with diffusion-based similarity fusion ontripartite graphs. Physica A 389,1259-1264(2010)

[38]  Q.-M. Zhang, M.-S. Shang, and L.Lü, Similarity-Based Classification in Partially Labeled Networks, Int.J. Mod. Phys. C 21, 813 (2010)

[39]  Q.-M. Zhang, M.-S. Shang, W. Zeng, Y. Chen, and L. Lü, Empirical comparison of local structural similarityindices for collaborative-filtering-based recommender systems, Physics Procedia3, 1887 (2010).

[40]  Z.-K. Zhang, T.Zhou, and Y.-C. Zhang, Personalized Recommendation via IntegratedDiffusion on User-Item-Tag Tripartite Graphs. Physica A, 389 , 179-186 (2010)

[41]  Z.-K. Zhang, C. Liu, Y.-C. Zhang, and T. Zhou, Solving the Cold-StartProblem in Recommender Systems with Social Tags. EPL 92 28002 (2010)

[42]  P. Wu, and Z.-K. Zhang. Enhancing personalized recommendation inweighted social tagging networks. Physical Procdia 3, 1877-1885(2010)

[43]  L. Lü, Link Prediction on Complex Networks (in Chinese),Journal of University of Electronic Science and Technology of China 39(5), 651 (2010).

[44]  L. Lü, Y.-C. Zhang, C. H. Yeung, and T. Zhou, Leaders in Social Networks, the delicious case,PLoS ONE 6(6): e21202 (2011).

[45]  L. Lü, D.-B. Chen,and T. Zhou, Small worldyields the most effective information spreading, New J. Phys. 13, 123005 (2011).

[46]  Z. Liu, Q.-M. Zhang, L. Lü, and T. Zhou,Link prediction in complex networks: a local naïve Bayes model, EPL 96, 48007 (2011).

[47]  W. Zeng, Y.-X. Zhu, L. Lü, and T. Zhou,Negative ratings play a positive role in information filtering, Physica A 390, 4486-4493 (2011)

[48]  H.-K. Liu, L. Lü, and T. Zhou,Uncovering the network evolution mechanism by link prediction, Sci Sin PhysMech Astron 41, 816-823 (2011)

[49] G. Cimini, M.Medo, T. Zhou, D. Wei, and Y.-C. Zhang, Heterogeneity, quality, and reputation in an adaptiverecommendation model, Eur. Phys.J. B 80, 201-208 (2011).

[50]  D. Wei, T. Zhou, G. Cimini, P. Wu,W. Liu, and Y.-C. Zhang, Effective mechanism for social recommendation ofnews, Physica A 390,2117-2126 (2011)

[51]  Y.-B. Zhou, T.Lei, T. Zhou, A robust ranking algorithm to spamming, EPL 94, 48002 (2011).

[52]  T. Zhou, M. Medo, G. Cimini,Z.-K. Zhang, and Y.-C. Zhang, Emergence of Scale-Free Leadership Structure in SocialRecommender Systems, PLoS ONE 6,e20648 (2011).

[53] J.-G. Liu, T. Zhou, and Q. Guo, Informationfiltering via biased heat conduction, Phys. Rev. E 84, 037101 (2011).

[54] T. Qiu, G. Chen, Z.-K. Zhang, and T. Zhou, An item-oriented recommendation algorithm on cold-start problem, EPL 95, 58003 (2011).

[55]   L. Lü, and W. Liu, Informationfiltering via preferential diffusion, Phys. Rev. E 83, 066119 (2011) .

[56]  Z.-K. Zhang, and C. Liu. Identifying the Role of SocialTags and its Application in Recommender Systems.  International Journal of Complex Systems inScience, 1 10 (2011)

[57]  L. Lü, and T. Zhou,Link prediction in complex networks: A survey, Physica A 390, 1150 (2011).

[58]  Z.-K. Zhang, T.Zhou, and Y.-C. Zhang, Tag-Aware Recommender systems: Astate-of-the-art survey. Journal of Computer Science and Technology 26, 767-777 (2011).

[59]  T. Qiu, G. Chen, Z.-K. Zhang, and T.Zhou, An Item oriented recommendation algorithm on cold start problem,EPL 95 58003 (2011).

[60]  L. Lü, M. Medo, C. H. Yeung, Y.-C. Zhang, Z.-K. Zhang, and T.Zhou, Recommender Systems, Physics Reports 519, 1-49 (2012).

[61]  D.-B. Chen, L.Lü, M.-S. Shang,Y.-C. Zhang, and T. Zhou,Identifying influential nodes in complex networks, Physica A 391, 1777-1787 (2012).

[62]  Y.-X. Zhu, L. Lü, Q.-M. Zhang, and T.Zhou, Uncovering missing links with cold ends, Physica A 391, 5769-5778 (2012).

[63]  Z. Yang, Z.-K.Zhang, and T. Zhou,Anchoring bias in online voting, EPL 100,68002 (2012).

[64]  H. Liu, F. Yu, A. Zeng, and L. Lü, Recommendation of leadersin online social systems, ISMIS’12 Lecture Notes in Artificial Intelligence 7661, 387-396 (2012).

[65]  Y.-B. Zhou, L. Lü, and M. Li, Quantifying the influence of scientistsand their publications: distinguishing between prestige and popularity, New J.Phys. 14, 033033 (2012).

[66]  Y.-X. Zhu, and L. Lü, Evaluation Metrics for Recommender Systems, Journalof University of Electronic Science and Technology of China 41, 163-175 (2012).

[67]  A. Zeng, L.Lü, T. Zhou,Manipulating directed networks for better synchronization, New J. Phys. 14, 083006 (2012).

[68]  Z.-K. Zhang, and C. Liu, Hybrid Recommendation Algorithmbased on two roles of social tags, International Journal of Bifurcation andChaos 22, 1250166 (2012).

[69]  G. Cimini, D.-B. Chen, L. Lü, M. Medo, Y.-C. Zhang, and T. Zhou, Enhancing topologyadaptation in information-sharing social networks, Physical Review E 85, 046108(2012)

[70] J. Huang, X.-Q.Cheng, H.-W. Shen, T. Zhou, and X. Jin, Exploring social influence via posterior effect ofword-of-mouth recommendations, WSDM'12, ACM Press, 2012, pages 573-582.

[71] 荣智海，唐明，汪小帆，吴枝喜，严钢，周涛，复杂网络2012年度盘点，电子科技大学学报41, 801-807 (2012)

[72]  A. Zeng, C.-H. Yeung, M.-S. Shang, and Y.-C. Zhang, The reinforcing influence ofrecommendations on global diversification, EPL 97, 18005(2012)

[73]  张子柯, 社会化标签系统的结构、演化和功能。上海理工大学学报32, 444-451(2012)

[74]  D.-B. Chen, and H. Gao, An Improved Adaptive model onInformation of Recommending and Spreading, Chinese Physics Letters 29, 048901(2012)

[75]  D.-B. Chen, H Gao, L.Lü*, and T. Zhou,Identifying influential nodes in large-scale directed networks: The role ofclustering, PLoS ONE 8, e77455(2013).

[76]  L. Lü, Z.-K. Zhang,and T. Zhou, Deviation ofZipf’s and Heaps’ Laws in Human Languages with Limited Dictionary Sizes, ScientificReports 3, 1082 (2013).

[77]  Q.-M. Zhang, L. Lü, W.-Q. Wang, Y.-X. Zhu, and T. Zhou, Potential Theory for Directed Networks, PLoS ONE 8(2), e55437 (2013).

[78]  Y. Zhou, L.Lü，W. Liu, and J. Zhang, The Power of Ground User inRecommender Systems，PLoS ONE 8,e70094 (2013).

[79]  F. Guo, Z.Yang, and T. Zhou, Predicting link directions via arecursive subgraph-based ranking, Physica A 392, 3402–3408 (2013).

[80]  Z.-D. Zhao, Z. Yang, Z.-K. Zhang, T. Zhou, Z.-G. Huang, and Y.-C. Lai, Emergence ofscaling in human-interest dynamics, Scientific Reports 3, 3472 (2013).

[81]  M. Zheng, L. Lü, and M. Zhao，Spreading in online social networks: The role of socialreinforcement, Phys. Rev. E 88,012818 (2013)

[82]  Z.-K. Zhang, Y. Sun, C.-X. Zhang, K. Fang, X. Xu, C. Liu,X. Wang, and K. Zhang. Diagnosing and Predicting the Earth's Health viaEcological Network Analysis. Discrete Dynamics in Nature and Society, 741318(2013)

[83]  T. Qiu, T.-T. Wang, Z.-K. Zhang, L.-X. Zhong, and G. Chen, Alleviating biasleads to accurate and personalized recommendation, EPL 10448007 (2013).

[84]  T. Qiu, Z.-K.Zhang, and G. Chen, Information Filtering via a Scaling-Based Function,PLoS ONE 8  e63531(2013).

[85]  D.-C. Nie,M.-J. Ding, Y. Fu, J.-L. Zhou, and Z.-K. Zhang, Social Interest ForUser Selecting Items in Recommender Systems, International Journal of ModenPhysics C 4 1350022(2013).

[86]  D.-D. Zhao, A. Zeng, M.-S. Shang, and J. Gao, Long-Term Effects of Recommendationon the Evolution of Online Systems, Chin. Phys. Lett. 30,118901(2013)

[87]  W. Zeng, A. Zeng, M.-S. Shang, and Y.-C. Zhang, Membership in social networksand the application in information filtering, EUROPEAN PHYSICAL JOURNAL B 86, 375(2013)

[88]  Y. Guan, D.-D. Zhao, A. Zeng, and M.-S. Shang, Preference ofonline users and personalized recommendations, Physica A 392,  3417-3423(2013)

[89]  Q.-M. Zhang, W. Zeng, A. Zeng, and M.-S. Shang, Extracting theInformation Backbone in Online System , PLoS ONE 8, e62624(2013)

[90]  Y.-W. Dong, S.-M. Cai, and M.-S. Shang, Empirical study onscaling of human behaviors in e-commerce, ACTA PHYSICA SINICA 62, 028901(2013)

[91]  G. Cimini, A. Zeng, M. Medo, and D.-B. Chen, The role of tasteaffinity in agent-based model for social recommendation, Advances in ComplexSystems, 1350009(2013)

[92]  D.-B. Chen, A. Zeng, G. Cimini, and Y.-C. Zhang,Adaptive social recommendation in a multiple category landscape, Eur. Phys. J.B 86, 61(2013)

[93]  王冠楠, 陈端兵, 傅彦, 新闻推荐的多维兴趣模型与传播分析. 计算机科学40, 126-130(2013)

[94]  王军，张子柯，基于社会化标签信息熵的个性化推荐算法, 图书情报工作57, 31-35(2013)