14年國際數(shù)學(xué)建模競賽2014試卷mcm summary sheet solution for team

1、ForofficeuseT1 T2 T3 T4 TeamControlProblemCForofficeuseF1 F2 F3 F4 2014 Mathematical Contest in Modeling (MCM) Summary Inanetwork,weoftenanalyzetheimportanceandinfluenceofaparticularnode by the relationship between the nodes.This question requires us to determine its influence through erdos co-autho

2、r networks and building an important document network model.and it applys to a series of disparate impact data network,.It can be utilized in reality, , for example, improve relationships, improve the industrys ModelThelarger“ImpactFactor”is, thegreaterinfluenceitThemoreauthoritative is,thegreaterin

3、fluence itWhiletheImpactFactorisamajorfactor, theauthorityisreferencefactors. And use them to construct influence models.We use a network science method in erdos1 to screen the useful information whas a node in the network model. Then,analyze the co-authored relationship between them, and reflect in

4、 the network model. Anothermethodistobuildanetworkmodelaboutthecorrelationbetweentheliteratu re, based on the assumption of the influence and determineing the greatest influence. Literature.，ased on the above network model,applyittothefieldofscienceandcirculationofChinesenewspapersglobalranki ng, bl

5、og page views and popularity, etc.And the results agreed well.Ouradvantagesbuildtherational,scientificmathematicalmodelonthebasisofanalyzingthearticl e clearly in the correct way.Establishahomographytoreflecttheinfluencenetwork,choosethedotprogramm ing, and it has some practical value.Disadvantageis

6、theconstraintconditionofthemodelissomewhatKeyImpactFactor QuestionOurtaskistofilterandanalyzetheusefuldata,andbuildthenetworkmodelof First, import the data “Erdos1 file” to excel , use the function of “vlookup” to filter out directly with Erdos1 researchers coauthored papers relations. Then, use the

7、 command of “Filter” .All data that are no direct relationship with Erdos will be deleted, use the remaining data modeling. In order to facilitate to identify the most influentialco-author,welimitthesizeofthenetwork,taking5co-co-authorstobuild the model. Finally, uses the graphviz 2.36 build the mod

8、el.Numbersofco-ALON,NOGAGRAHAM,RONALDHARARY,BOLLOBAS,RODL,SeeGraphGraphQuestionQuestionrestated:CreateainfluencemeasuresbasedonthefirstnetworkmodeltodeterminewhointhisErdos1networkhassignificantinfluence within the network.ModelAccordingtothemeasurementabouttheimportanceofnodesinthegraphpaper and us

9、ing of social analysis, give the importance of criteria: take the number ofco-authorsinto major considerationand takethe number ofimportant worksintosecondaryconsiderations,thenmakeaweightedAccordingtoErdosnumbertheory,themoreimportantworkscoauthoredwith Erds, the greater it impacts.Because of the e

10、quality between the co-author, each co-author is one equivalent.Whilethenumberofworksarekequivalents(k1positive).Assumingtheimportanceof “W,apersonhasaco-authorofawork,bworks,ithasW=Solution:.Coauthoredwithexcelsortoutthenumberofranking,thenumberpublishedliteratureandrankings.Basedontheassumptionoft

11、heimportanceofthe rules into data discovery, we find that ALON, NOGA M. is the most important.Numbersofco-ALON,NOGAGRAHAM,RONALDHARARY,BOLLOBAS,RODL,ALON,NOGAM.isthemostModelFirst, Import the appendix of 16 documents to excel, search each documents citations in Google scholar and record the citation

12、s included in the16 documents. Processthedataandgiveanumberwhichiscorrespondtoadocument(seeAppendix excel document), and build models by graphviz2.36 .CorrespondingofthenumberandThemoretimesitis cited,the moreimportantthe documentAccordingtothe4thdocumentcitedmostfrequently,sothe4thdocumentisthe mos

13、t important.Through our algorithm, we know that the regional analysis of each network researchers is more associated with the node, the more important he plays the role. 3.AccordingtothedataoftheChinaInstituteofScientificandTechnicalInformation and Engineering Index (EI) included the number of paper

14、s published by Tsinghua University, since 1993, ithas maintained the nations first university for 18 years and its papers are included in Science Citation Index (SCI Online ) ,.While the number increases stable in recent years, its quality has steadily improved; The number of TsinghuaUniversitypaper

15、scitedbySCIpapersamongthebestuniversitiesislargest in the country continues.AsthepaperhasbeencitedChinarankedfirst,byouralgorithm,wecanseeTsinghua University plays the most important role in engineering sciences network, the fact is QuestionQuestionApplythealgorithmtoaparticularsetofnetworkmodel,and

16、itshouldbelimitedto a particulartype of network,locationand predetermined size to ensure the correctness of the algorithm application.Restrict:SpecifictypesofLocation:Booksize:RecentAlgorithm:Todeterminetheirimportanceandinfluencebyanalyzingthedeman d rate and the number of the people concerned them

17、.Theimportanceisreflectedinthesalesofnewspapersandmagazines.According to the importance of rules and algorithms,themorevolume,thegreatertheirinfluence.BySearchNewsNetwork HYPERLINK http:/www.sobao.n/ (www.sobao.n et),wesearchthelargecirculation newspapers inChina,findingthatthemoresales,t he worlds

18、more high ranking, the greater the impact,in line with the algorithm. (Data see circulation of newspaper.xls)ranking rangking the 參考消16人民日2環(huán)球時3揚子晚4中國電視5廣州日6體壇周7南方都市8齊魯晚9信息時南方周楚天都市金陵晚新民晚北京晚羊城晚華西都市新快報 成都商燕趙都市Theblogsinfluencedepends onthepagetimesof零數(shù)據(jù)化管理工具（一）：周銷售指數(shù)據(jù)化管理：幾張非常有用的銷售分析圖如何用EXCEL做四象限分析零售業(yè)如何

19、通過數(shù)據(jù)挖掘 VIP 顧客的價值（一如何做買贈的促銷活動數(shù)據(jù)分析（一）平衡點確塔吉特讀心術(shù)用戶數(shù)據(jù)分析的魔力【轉(zhuǎn)數(shù)據(jù)化管理系列：什么是數(shù)據(jù)化管和數(shù)據(jù)有關(guān)的經(jīng)典視化繁為簡的管理工具：四象限分析零售業(yè)數(shù)據(jù)化管理工具（三）：目標(biāo)銷售目標(biāo)分By the website HYPERLINK /blog/static/7593898420127144146946/) Tenpopularblogs,statisticaleveryonesviews,accordingtotheimportanceofrulesa nd algorithms, and count most as the most infl

20、uential blog.QuestionQuestionThroughnetworkingmodel toimprove Select the person to improve their own mathematics influenceassoonaspossible by the algorithm.HelpustomakechoicesaboutthefuturementorthroughtheIt found that many real networks have a common naturet hat is community structure. That means t

21、he entire network is composed of a number of groups .The connection of internal nodes in one group is very close, and the connection between groups is relatively sparse. (Reference network community structure based on weighted similarity partitioning algorithm node).Using the node structure similarity to divide groups is clearly aware of their own respective country belongs area and clears their exchanges and cooperation.Byseekingcontac