计算机科学 ›› 2021, Vol. 48 ›› Issue (9): 95-102.doi: 10.11896/jsjkx.200700097
所属专题： 智能数据治理技术与系统
李思颖1, 徐杨1, 王欣2, 赵若成3
LI Si-ying1, XU Yang1, WANG Xin2, ZHAO Ruo-cheng3
摘要： 随着运输技术的快速发展,铁路已成为人们出差、度假、探亲时选择的主要出行方式之一。与此同时,旅客共同出行(以下简称同行)的行为特征也越来越普遍。依据旅客间的同行关系,可以构建同行关系网络;而对该网络中潜在的链接进行预测,将有助于提供个性化的服务和产品。为此,文中提出一种原创的方法,用于在旅客同行关系网络中发现潜在的同行关系。首先对传统的图模式关联规则进行扩展,提出了两类“同行图模式关联规则”,用于预测新的同行关系和未来的同行频次。然后,将上述规则挖掘计算的问题分解为频繁同行模式挖掘、规则生成以及关联分析3个子问题,并设计了有效的分布式和集中式的算法。通过在大规模真实数据集上的测试,证明了所提方法能够高效且准确地预测旅客同行关系网络中潜在的同行关系,且两类规则的预测准确率均高于50%,远高于传统方法(如Jaccard的预测准确率为24%)。
中图分类号:
| [1]FENG X,LI Y,CHEN H M.Research on Constructing SocialNetwork of Airline Customers From Data of PNR[J].Computer Simulation,2013,30(6):51-54. [2]JIA X G,ZHOU Y W,LIN Y F.A relationship classification algorithm for the social networks of civil aviation passengers[J].Journal of Beijing Jiaotong University,2013,37(6):103-106. [3]CHEN D,WU G,LI Y H,et al.Review on Passenger Service Management Research[J].Journal of Transportation Enginee-ring and Information,2017,15(2):14-20. [4]ZHANG Y T.Predicting the Family Travels Based on Passenger Social Networks[J].Modern Computer,2016(8):1007-1423. [5]LARSEN J,URRY J,AXHAUSEN K W.Networks andtourism:Mobile Social Life[J].Annals of Tourism Research,2007,34(1):244-262. [6]LIN Y F,WAN H Y,JIANG R,et al.Inferring travel purposes of passenger groups for better understanding of passengers[J].IEEE Transactions on Intelligent Transportation System,2015,16(1):235-243. [7]LIN Y F,JIA X G,LIN M G,et al.Inferring High Quality Co-Travel Networks[C]//CAAC.2013. [8]CHEN Y Y,CHENG A J,WINSTON H.Travel Recommendation by Mining People Attributes and Travel Group Types From Community-Contributed Photos[J].IEEE Transaction on Multimedia,2013,15(6):1283-1295. [9]GENG W,YANG G.Partial Correlation between Spatial andTemporal Regularities of Human Mobility[J].Scientific Reports,2017,7(1). [10]YE S G.Research on link prediction in co-travel network[D].Beijing:Beijing Jiaotong University,2016. [11]AGRAWAL R,IMIELINSKI T,SWAMI A.Mining association rules between sets of items in large databases[C]//Acm Sigmod Record.ACM 1993,22(2):207-216. [12]FAN W F,WANG X,WU Y H,et al.Association rules with graph patterns[J].PVLDB,2015,8(12):1502-1513. [13]WANG X,XU Y.Mining Graph Pattern Association Rules[C]//International Conference on Database and Expert Systems Applications.Springer,Cham,2018. [14]JIANG H,LIU Z,LIU C,et al.Community detection in complex networks with an ambiguous structure using central node based link prediction[J].Knowledge-Based Systems,2020,195:105626. [15]WU J H,SHEN J,ZHOU B.Community Features Based Ba-lanced Modularity Maximization Social Link Prediction Model[J].Computer Science,2019,46(3):253-259. [16]YANG X H,YU J,ZHANG R.Link Prediction Method Based on Local Community and Nodes' Relativity[J].Computer Science,2019,46(1):155-161. [17]CAI L,JI S W.A Multi-Scale Approach for Graph Link Prediction[J].The Thirty-Fourth AAAI Conference on Artifificial Intelligence (AAAI-20),2020,34(4):3308-3315. [18]LI X,SHANG Y,CAO Y,et al.Type-Aware Anchor Link Prediction across Heterogeneous Networks Based on Graph Attention Network[J].The Thirty-Fourth AAAI Conference on Artifificial Intelligence (AAAI-20),2020,34(1):147-155. [19]SHU J,ZHANG X P,LUO X Y,et al.Link Prediction for Opportunistic Networks Based on Deep Learning[J].Journal of Software,2016,27(1):36-48. [20]WU X M,WU J S,LI Y F,et al.Link prediction of time-evolving network based on node ranking[J].Knowledge-Based Systems,2020,195:105740. [21]ELSEIDY M,ABDELHAMID E,SKIADOPOULOS S,et al.GRAMI:frequent sub-graph and pattern mining in a single large graph[J].PVLDB,2014,7(7):517-528. [22]FIEDLER M,BORGELT C.Subgraph Support in a Single Large Graph[C]//IEEE International Conference on Data Mining Workshops.IEEE,2007. [23]GUDES E,SHIMONY S E,VANETIK N.Discovering Frequent Graph Patterns Using Disjoint Paths[J].IEEE Transactions on Knowledge and Data Engineering,2006,18(11):1441-1456. [24]YAN X,HAN J.gSpan:Graph-Based Substructure Pattern Mi-ning[C]//IEEE Computer Society.2002. [25]GOUDA K,ZAKI M J.Efficiently Mining Maximal Frequent Itemsets[C]//IEEE International Conference on Data Mining.IEEE,2002. [26]HUAN J,WANG W,PRINS J.Spin:Mining maximal frequent subgraphs from graph databases[C]//Tenth Acm Sigkdd International Conference on Knowledge Discovery & Data Mining.ACM,2004. [27]JACCARD P.Etede de la distribution florale dans une portion des Alpes et du Jura[J].Bulletin De La Societe Vaudoise Des Sciences Natuerlles,1901,37(142):547-579. |
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