[1]张学龙,王军进.链路预测下能源供应链网络合作演化机制研究[J].智能系统学报,2017,12(2):221-228.[doi:10.11992/tis.201605003]
ZHANG Xuelong,WANG Junjin.On the evolution cooperation mechanism of energy supply chain networks under link prediction[J].CAAI Transactions on Intelligent Systems,2017,12(2):221-228.[doi:10.11992/tis.201605003]
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ZHANG Xuelong,WANG Junjin.On the evolution cooperation mechanism of energy supply chain networks under link prediction[J].CAAI Transactions on Intelligent Systems,2017,12(2):221-228.[doi:10.11992/tis.201605003]
链路预测下能源供应链网络合作演化机制研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
12
期数:
2017年第2期
页码:
221-228
栏目:
学术论文—智能系统
出版日期:
2017-05-05
- Title:
- On the evolution cooperation mechanism of energy supply chain networks under link prediction
- Keywords:
- supply chain network; cooperation evolution; link prediction; network structure; energy supply chain; similarity index; accuracy; coupling
- 分类号:
- TP391;F273
- 摘要:
- 应用供应链网络结构或节点的属性信息预测未产生链接的节点企业合作的可能性是链路预测应用供应链网络合作演化分析的关键，利用链路预测的理论框架和评价方法，借助5种相似性指标对能源供应链网络合作连边演化进行预测。研究结果表明：当使用供应链网络结构属性作为单一相似性指标时，RWR指标的预测效果最好；耦合指标预测精确度要比单独考虑单一指标时有显著提高；RWR指标和Katz指标耦合效果要优于和CN指标、PA指标、LP指标耦合效果，且RWR指标在耦合算法中起到主导性作用；与直接建立网络演化模型相比，链路预测在分析供应链网络合作演化机制上更为有效。
- Abstract:
- Using attribute information of a given network structure or nodes of a supply chain to predict the possibility of cooperation with an unlinked enterprise is key to link prediction being applied to supply chain networks. As such, we predicted side-connected evolutions of network cooperation in energy supply chains by utilizing a theoretical framework and evaluation method for link prediction and five similarity indexes. Our results show that when the attribute of the network structure of a supply chain is used as a single similarity index, the predictive effect of the RWR index is the best. Further, the prediction accuracy of the coupling index is remarkably higher than the single index. Here, the coupling effect between the RWR index and the Katz index is superior to the coupling effects between RWR and CN, PA and LP index. Further, the RWR index plays a leading role in the coupling algorithm. Compared with directly setting up a model for network evolution, link prediction is more effective in analyzing the cooperation evolution mechanism of supply chain networks.
- 参考文献/References:
-
[1] SARUKKAI R R. Link prediction and path analysis using Markov chains[J]. Computer networks, 2000, 33(1/2/3/4/5/6): 377-386.
[2] ZHU Jianhan, HONG Jun, HUGHES J G. Using Markov chains for link prediction in adaptive web sites[M]//BUSTARD D, LIU Weiru, STERRITT R. Soft-Ware 2002: Computing in An Imperfect World. Berlin Heidelberg: Springer, 2002: 60-73.
[3] POPESCUL A, UNGAR L H. Statistical relational learning for link prediction[C]//Proceedings of Workshop on Learning Statistical Models from Relational Data. New York: ACM Press, 2003: 81-87.
[4] KOLACZYK E E. Some implications of path-based sampling on the Internet[C]//Proceedings of a Workshop on Statistics of Networks. Washington: National Academies Press, 2007: 207-226.
[5] 刘宏鲲, 吕琳媛, 周涛. 利用链路预测推断网络演化机制[J]. 中国科学: 物理学 力学 天文学, 2011, 41(7): 816-823. LIU Hongkun, LYU Linyuan, ZHOU Tao. Uncovering the network evolution mechanism by link prediction[J]. Scientia sinica: physica, mechanica & astronomica, 2011, 41(7): 816-823.
[6] O’MADADHAIN J, HUTCHINS J, SMYTH P. Prediction and ranking algorithms for event-based network data[J]. ACM SIGKDD explorations newsletter, 2005, 7(2): 23-30.
[7] LIN Dekang. An information-theoretic definition of Similarity[C]//Proceedings of the Fifteenth International Conference on Machine Learning. San Francisco: Morgan Kaufmann Publishers, 1998: 296-304.
[8] 吕琳媛. 复杂网络链路预测[J]. 电子科技大学学报, 2010, 39(5): 651-661. Lü Linyuan. Link prediction on complex networks[J]. Journal of university of electronic science and technology of China, 2010, 39(5): 651-661.
[9] Lü Linyuan, ZHOU Tao. Link prediction in complex networks: a survey[J]. Physica A: statistical mechanics and its applications, 2011, 390(6): 1150-1170.
[10] GETOOR L, DIEHL C P. Link mining: a survey[J]. ACM SIGKDD explorations newsletter, 2005, 7(2): 3-12.
[11] LIBEN-NOWELL D, KLEINBERG J. The link prediction problem for social networks[J]. Journal of the American society for information science and technology, 2007, 58(7): 1019-1031.
[12] ZHOU Tao, Lü Linyuan, ZHANG Yicheng. Predicting missing links via local information[J]. The European physical journal B, 2009, 71(4): 623-630.
[13] CLAUSET A, MOORE C, NEWMAN M E J. Hierarchical structure and the prediction of missing links in networks[J]. Nature, 2008, 453(7191): 98-101.
[14] GUIMERà R, SALES-PARDO M. Missing and spurious interactions and the reconstruction of complex networks[J]. Proceedings of the national academy of sciences of the United States of America, 2009, 106(52): 22073-22078.
[15] ADAMIC L A, HUBERMAN B A. Power-law distribution of the world wide web[J]. Science, 2000, 287(5461): 2115.
[16] NEWMAN M E J. The structure of scientific collaboration networks[J]. Proceedings of the national academy of sciences of the United States of America, 2001, 98(2): 404-409.
[17] ALBERT R, BARABáSI A L. Statistical mechanics of complex networks[J]. Reviews of modern physics, 2002, 74(1): 47-97.
[18] DOROGOVTSEV S N, MENDES J F F. Evolution of networks[J]. Advances in physics, 2002, 51(4): 1079-1187.
[19] BARABáSI A L, Albert R. Emergence of scaling in random networks[J]. Science, 1999, 286(5439): 509-512.
[20] HANLEY J A, MCNEIL B J. The meaning and use of the area under a receiver operating characteristic (ROC) curve[J]. Radiology, 1982, 143(1): 29-36.
[21] HERLOCKER J L, KONSTAN J A, TERVEEN L G, et al. Evaluating collaborative filtering recommender systems[J]. ACM transactions on information systems, 2004, 22(1): 5-53.
[22] ZHOU Tao, REN Jie, MEDO M, et al. Bipartite network projection and personal recommendation[J]. Physical review E, 2007, 76(4): 046115.
[23] FAWCETT T. An introduction to ROC analysis[J]. Pattern recognition letters, 2006, 27(8): 861-874.
[24] LORRAIN F, WHITE H C. Structural equivalence of individuals in social networks[J]. The journal of mathematical sociology, 1971, 1(1): 49-80.
[25] XIE Yanbo, ZHOU Tao, WANG Binghong. Scale-free networks without growth[J]. Physica A: statistical mechanics and its applications, 2008, 387(7): 1683-1688.
[26] Lü Linyuan, JIN Cihang, ZHOU Tao. Similarity index based on local paths for link prediction of complex networks[J]. Physical review E, 2009, 80(4): 046122.
[27] KATZ L. A new status index derived from sociometric analysis[J]. Psychometrika, 1953, 18(1): 39-43.
[28] BRIN S, PAGE L. The anatomy of a large-scale hypertextual Web search engine[J]. Computer networks and ISDN systems, 1998, 30(1): 107-117.
备注/Memo
收稿日期:2016-5-3;改回日期:。
基金项目:国家自然科学基金项目（71662007）；广西哲学社会科学研究课题（15BJY016）；桂林电子科技大学研究生教育创新计划项目（2016YJCX61）．
作者简介:张学龙,男,1978年生,副教授,博士,主要研究方向为供应链管理、工业工程、决策分析;王军进,男,1990年生,硕士研究生,主要研究方向为供应链管理。
通讯作者:王军进. E-mail:1204703207@qq.com.
更新日期/Last Update:
1900-01-01