动态供应链网络中企业合作关系的链路预测

doi:10.3778/j.issn.1002-8331.2007-0398

摘要/Abstract

摘要： 随着经济全球化的推进，供应链由传统的单链形式逐渐演变成供应链网络。这类复杂供应链网络由不同类型的企业构成，为了适应外部环境的变化，企业与企业之间以供需为目的形成的合作关系随着时间不断改变。致力于动态供应链网络中企业合作关系的预测，提出一种基于投影和时间事件的链路预测模型，将动态供应链网络划分为由若干个时间片组成的网络快照，通过对网络快照进行投影分析，形成潜在合作链路集合，基于时间事件计算集合里每个元素的链路预测得分，比较得分确定预测结果。实验分析结果表明，相对传统链路预测算法，所提算法在动态供应链网络中预测企业间未来合作关系具有更好的预测效果，且通过改变网络快照的大小和时间事件变化率可以进一步提高其预测精度。

关键词: 动态供应链网络, 合作关系, 链路预测

Abstract: With the advancement of economic globalization, the supply chain has gradually evolved from a traditional single-chain form to a supply chain network. This type of complex supply chain network is composed of different types of enterprises. In order to adapt to the changes in the external environment, the cooperation relationship formed with enterprises for the purpose of supply and demand continues to change over time. Committed to the prediction of enterprise cooperation relationship in the dynamic supply chain network, a link prediction model based on projections and temporal events is proposed. The dynamic supply chain network is divided into network snapshots consisting of several time slices. Through projection analysis of the network snapshots, a set of potential cooperation links is formed. The prediction score of each element in the set is calculated based on the temporal event. Comparing the prediction scores, the results are attained. Experimental analysis results show that, compared with traditional link prediction algorithms, the proposed algorithm has a better prediction effect in predicting the future cooperation relationship between enterprises in a dynamic supply chain network, and it is concluded that the prediction precision can be further improved by changing the size of the network snapshot and the change rate of temporal events.

Key words: dynamic supply chain network, cooperation relationship, link prediction

卢志刚, 陈倩. 动态供应链网络中企业合作关系的链路预测[J]. 计算机工程与应用, 2022, 58(2): 265-273.

LU Zhigang, CHEN Qian. Link Prediction of Enterprise Cooperation Relationship in Dynamic Supply Chain Network[J]. Computer Engineering and Applications, 2022, 58(2): 265-273.

参考文献

[1] 张炳轩，李龙洙，都忠诚.动态供应链合作伙伴的评价体系及其模糊评价方法[J].天津师范大学学报（自然科学版），2001（3）：19-23.
ZHANG B X，LI L Z，DU Z C.Evaluation system and fuzzy evaluation method of dynamic supply chain partners[J].Journal of Tianjin Normal University（Natural Science Edition），2001（3）：19-23.
[2] ZEYDAN M，COLPAN C，COBANOGLU C.A combined methodology for supplier selection and performance evaluation[J].Expert Systems with Applications，2011，38（3）：2741-2751.
[3] HA S H，KRISHNAN R.A hybrid approach to supplier selection for the maintenance of a competitive supply chain[J].Expert Systems with Applications，2008，34（2）：1303-1311.
[4] SARI B，SEN T，KILIC S.Ahp model for the selection of partner companies in virtual enterprises[J].International Journal of Advanced Manufacturing Technology，2008，38：367-376.
[5] ASLI A，?ZTURK N.Supplier selection and performance evaluation in just-in-time production environments[J].Expert Systems with Applications，2011，38（5）：6351-6359.
[6] 吕亚楠，韩华，贾承丰，等.基于节点亲密度的链路预测算法[J].计算机科学，2018，45（11）：92-96.
LU Y N，HAN H，JIA C F，et al.Link prediction algorithm based on node intimite degree[J].Computer Sciences，2018，45（11）：92-96.
[7] LU L Y，ZHOU T.Link prediction in complex networks：a survey[J].Physica A：Statistical Mechanics and Its Applications，2011，390（6）：1150-1170.
[8] AGHABOZORGI F，KHAYYAMBASHI M R.A new similarity measure for link prediction based on local structures in social networks[J].Physica A：Statistical Mechanics and Its Applications，2018，501：12-23.
[9] NAJARI S，SALEHI M，RANJBAR V，et al.Link prediction in multiplex networks based on interlayer similarity[J].Physica A：Statistical Mechanics and Its Applications，2019：120978.
[10] YAO Y，ZHANG R，YANG F，et al.Link prediction in complex networks based on the interactions among paths[J].Physica A：Statistical Mechanics and Its Applications，2018，510：52-67.
[11] XU Z，PU C，YANG J.Link prediction based on path entropy[J].Physica A：Statistical Mechanics and Its Applications，2016，456：294-301.
[12] LI J C，GE B F，YANG K W，et al.Meta-path based heterogeneous combat network link prediction[J].Physica A：Statistical Mechanics and Its Applications，2017，482：507-523.
[13] 王文涛，黄烨，吴淋涛，等.基于改进随机游走的网络表示学习算法[J].计算机应用，2019，39（3）：651-655.
WANG W T，HUANG Y，WU L T，et al.Network representation learning algorithm based on improved random walk[J].Journal of Computer Applications，2019，39（3）：651-655.
[14] CURADO M.Return random walks for link prediction[J].Information Sciences，2020，510：99-107.
[15] 吕亚楠，韩华，贾承丰，等.基于有偏向的重启随机游走链路预测算法[J].复杂系统与复杂性科学，2018，15（4）：17-24.
LV Y N，HAN H，JIA C F，et al.Link prediction algorithm based on biased random walk with restart[J].Complex System and Complexity Science，2018，15（4）：17-24.
[16] ALLALI O，MAGNIEN C，LATAPY M.Link prediction in bipartite graphs using internal links and weighted projection[C]//Proceedings of the 2011 IEEE Conference on Computer Communications Workshops（INFOCOM WKSHPS），Shanghai，China，Apr 10-15，2011.Piscataway，NJ，USA：IEEE，2011：936-941.
[17] GAO M，CHEN L，LI B，et al.Projection-based link prediction in a bipartite network[J].Information Sciences，2017，376：158-171.
[18] PUJARI M，KANAWATI R.Supervised rank aggregation approach for link prediction in complex networks[C]//Proceedings of the 21st International Conference on World Wide Web，Lyon，France，Apr，2012.New York，NY，USA：Association for Computing Machinery，2012：1189-1196.
[19] DUNLAVY D，KOLDA T，ACAR E.Temporal link prediction using matrix and tensor factorizations[J].ACM Trans Knowl Discov Data，2011，5（2）：1-27.
[20] VU D Q，ASUNCION A U，HUNTER D R，et al.Continuous-time regression models for longitudinal networks[C]//Proceedings of the 24th International Conference on Neural Information Processing Systems，Granada，Spain，Dec，2011.Red Hook，NY，USA：Curran Associates Inc，2011：2492-2500.
[21] XU A，ZHENG X.Dynamic social network analysis using latent space model and an integrated clustering algorithm[C]//Proceedings of the 2009 Eighth IEEE International Conference on Dependable，Autonomic and Secure Computing，Chengdu，China，Dec 12-14，2009.Piscataway，NJ，USA：IEEE，2009：620-625.
[22] LAHIRI M，BERGER-WOLF T Y.Structure prediction in temporal networks using frequent subgraphs[C]//Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining，Honolulu，HI，Mar 1-Apr 5，2007.Piscataway，NJ，USA：IEEE，2007：35-42.
[23] ASLAN S，KAYA B，KAYA M.Predicting potential links by using strengthened projections in evolving bipartite networks[J].Physica A：Statistical Mechanics and Its Applications，2019，525：998-1011.
[24] DONG X，MAVROEIDIS D，CALABRESE F，et al.Multiscale event detection in social media[J].Data Mining and Knowledge Discovery，2015，29：1374-1405.
[25] SETT N，BASU S，NANDI S，et al.Temporal link prediction in multi-relational network[J].World Wide Web，2018，21（2）：395-419.
[26] ASLAN S，KAYA B.Time-aware link prediction based on strengthened projection in bipartite networks[J].Information Sciences，2020，506：217-233.
[27] AHMED N M，CHEN L，WANG Y L，et al.Sampling-based algorithm for link prediction in temporal networks[J].Information Sciences，2016，374：1-14.
[28] O’MADADHAIN J，HUTCHINS J，SMYTH P.Prediction and ranking algorithms for event-based network data[J].SIGKDD Explor Newsl，2005，7（2）：23-30.
[29] IBRAHIM N M A，CHEN L.Link prediction in dynamic social networks by integrating different types of information[J].Applied Intelligence，2015，42：738-750.
[30] TYLENDA T，ANGELOVA R，BEDATHUR S.Towards time-aware link prediction in evolving social networks[C]//Proceedings of the 3rd Workshop on Social Network Mining and Analysis，Paris，France，Jun，2009.New York，NY，USA：Association for Computing Machinery，2009：1-10.
[31] ASLAN S，KAYA M.Topic recommendation for authors as a link prediction problem[J].Future Generation Computer Systems，2018，89：249-264.