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searchindex.js
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Search.setIndex({docnames:["applications/ch10/anomaly-detection","applications/ch10/ch10","applications/ch10/significant-communities","applications/ch10/significant-edges","applications/ch10/significant-vertices","applications/ch8/ch8","applications/ch8/community-detection","applications/ch8/model-selection","applications/ch8/out-of-sample","applications/ch8/single-vertex-nomination","applications/ch8/testing-differences","applications/ch8/vertex-nomination","applications/ch9/ch9","applications/ch9/graph-matching-vertex","applications/ch9/multiple-vertex-nomination","applications/ch9/two-sample-hypothesis","coverpage","foundations/ch1/ch1","foundations/ch1/examples-of-applications","foundations/ch1/exercises","foundations/ch1/main-challenges","foundations/ch1/types-of-learning-probs","foundations/ch1/types-of-networks","foundations/ch1/what-is-a-network","foundations/ch1/why-study-networks","foundations/ch2/big-picture","foundations/ch2/ch2","foundations/ch2/discover-and-visualize","foundations/ch2/fine-tune","foundations/ch2/get-the-data","foundations/ch2/prepare-the-data","foundations/ch2/select-and-train","foundations/ch2/transformation-techniques","foundations/ch3/big-picture","foundations/ch3/ch3","foundations/ch3/discover-and-visualize","foundations/ch3/get-the-data","foundations/ch3/prepare-the-data","introduction/preface","introduction/terminology","representations/ch4/ch4","representations/ch4/matrix-representations","representations/ch4/network-representations","representations/ch4/properties-of-networks","representations/ch4/regularization","representations/ch5/ch5","representations/ch5/models-with-covariates","representations/ch5/multi-network-models","representations/ch5/single-network-models","representations/ch5/single-network-models_ER","representations/ch5/single-network-models_RDPG","representations/ch5/single-network-models_SBM","representations/ch5/single-network-models_theory","representations/ch5/why-use-models","representations/ch6/ch6","representations/ch6/estimating-parameters","representations/ch6/estimating-parameters_mle","representations/ch6/estimating-parameters_spectral","representations/ch6/estimating-parameters_theory","representations/ch6/graph-neural-networks","representations/ch6/joint-representation-learning","representations/ch6/multigraph-representation-learning","representations/ch6/random-walk-diffusion-methods","representations/ch6/spectral-embedding","representations/ch6/spectral_","representations/ch6/why-embed-networks","representations/ch7/ch7","representations/ch7/theory-matching","representations/ch7/theory-multigraph","representations/ch7/theory-single-network","spectral-embedding"],envversion:{"sphinx.domains.c":2,"sphinx.domains.changeset":1,"sphinx.domains.citation":1,"sphinx.domains.cpp":3,"sphinx.domains.index":1,"sphinx.domains.javascript":2,"sphinx.domains.math":2,"sphinx.domains.python":2,"sphinx.domains.rst":2,"sphinx.domains.std":2,"sphinx.ext.intersphinx":1,"sphinxcontrib.bibtex":7,sphinx:56},filenames:["applications/ch10/anomaly-detection.ipynb","applications/ch10/ch10.ipynb","applications/ch10/significant-communities.ipynb","applications/ch10/significant-edges.ipynb","applications/ch10/significant-vertices.ipynb","applications/ch8/ch8.ipynb","applications/ch8/community-detection.ipynb","applications/ch8/model-selection.ipynb","applications/ch8/out-of-sample.ipynb","applications/ch8/single-vertex-nomination.ipynb","applications/ch8/testing-differences.ipynb","applications/ch8/vertex-nomination.ipynb","applications/ch9/ch9.ipynb","applications/ch9/graph-matching-vertex.ipynb","applications/ch9/multiple-vertex-nomination.ipynb","applications/ch9/two-sample-hypothesis.ipynb","coverpage.md","foundations/ch1/ch1.ipynb","foundations/ch1/examples-of-applications.ipynb","foundations/ch1/exercises.ipynb","foundations/ch1/main-challenges.ipynb","foundations/ch1/types-of-learning-probs.ipynb","foundations/ch1/types-of-networks.ipynb","foundations/ch1/what-is-a-network.ipynb","foundations/ch1/why-study-networks.ipynb","foundations/ch2/big-picture.ipynb","foundations/ch2/ch2.ipynb","foundations/ch2/discover-and-visualize.ipynb","foundations/ch2/fine-tune.ipynb","foundations/ch2/get-the-data.ipynb","foundations/ch2/prepare-the-data.ipynb","foundations/ch2/select-and-train.ipynb","foundations/ch2/transformation-techniques.ipynb","foundations/ch3/big-picture.ipynb","foundations/ch3/ch3.ipynb","foundations/ch3/discover-and-visualize.ipynb","foundations/ch3/get-the-data.ipynb","foundations/ch3/prepare-the-data.ipynb","introduction/preface.ipynb","introduction/terminology.ipynb","representations/ch4/ch4.ipynb","representations/ch4/matrix-representations.ipynb","representations/ch4/network-representations.ipynb","representations/ch4/properties-of-networks.ipynb","representations/ch4/regularization.ipynb","representations/ch5/ch5.ipynb","representations/ch5/models-with-covariates.ipynb","representations/ch5/multi-network-models.ipynb","representations/ch5/single-netwo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Detection For Timeseries of Networks","Applications for Many Networks","Testing for Significant Communities","Testing for Significant Edges","Testing for Significant Vertices","Applications When You Have One Network","Community Detection","Model Selection","Out-of-sample Embedding","Single-Network Vertex Nomination","Testing for Differences between Communities","Single-Network Vertex Nomination","Applications for Two Networks","Graph Matching","Vertex Nomination For Multiple Networks","Two-Sample Hypothesis Testing","HANDS-ON NETWORK MACHINE LEARNING WITH GRASPOLOGIC AND SCIKIT-LEARN","The Network Machine Learning Landscape","Examples of applications","Exercises","Main Challenges of Network Learning","Types of Network Learning Problems","Types of Networks","What Is A Network?","Why Study Networks?","Look at the big picture","End-to-end Biology Network Machine Learning Project","Discover and Visualize the Data to Gain Insights","Fine-Tune your Model","Get the Data","Prepare the Data for Network Algorithms","Select and Train a Model","Transformation Techniques","Look at the Big Picture","End-to-end Business Network Machine Learning Project","Discover and Visualize the Data to Gain Insights","Get the Data","Prepare the Data for Network Algorithms","Front Matter","Terminology and Math Refresher","Properties of Networks as a Statistical Object","Matrix Representations Of Networks","Representations of Networks","Properties of Networks","Regularization","Why Use Statistical Models?","Network Models with Covariates","Multiple Network Models","Network Models","Erd\u00f6s-R\u00e9nyi (ER) Random Networks","Random Dot Product Graphs (RDPG)","Stochastic Block Models (SBM)","Single network model theory","Why Use Statistical Models?","Learning Network Representations","Estimating Parameters in Network Models","Estimating Parameters in Network Models via MLE","Estimating Parameters in Network Models via Spectral Methods","Model Estimation Theory","Graph Neural Networks","Joint Representation Learning","Multiple-Network Representation Learning","Random-Walk and Diffusion-based Methods","Spectral Embedding Methods","When Should You Use ASE vs LSE?","Why embed networks?","Theoretical Results","Theory for Graph Matching","Theory for Multiple-Network Models","Theory for Single Network Models","Spectral Embedding Methods"],titleterms:{"abstract":38,"break":[63,70],"class":52,"erd\u00f6":[49,52,55,56],"function":[39,44],"r\u00e9nyi":[49,52,55,56],AND:16,ASE:[63,64,70],For:[0,14,61],Into:[63,70],One:[5,39],The:[0,8,17,41,43,44,47,49,50,51,53,55,58,61,63,65,70],Use:[45,53,61,64],Useful:39,Using:[8,60,61,63,70],WITH:16,With:[0,63,70],about:[43,47,63,65,70],acknowledg:38,adjac:[41,43,44,61,63,70],advanc:39,algorithm:[30,37,63,70],alien:61,all:61,allow:47,anomali:0,anywai:65,applic:[1,5,12,18],approach:0,approxim:[63,70],aren:[52,53],aspect:47,assign:51,assist:60,assort:60,attribut:43,augment:44,automat:60,averag:61,base:62,becom:[63,70],between:[10,41,44,51],bia:44,big:[25,33],bigger:[63,70],binari:[44,52],biologi:26,block:[50,51,52,55,56,57,60,65],book:43,bootstrap:0,build:63,busi:34,can:[43,50,61,63,70],care:[53,63,70],challeng:20,cluster:43,coeffici:43,coin:[53,55],collect:[47,61],column:13,combin:61,common:47,commun:[2,6,10,51,55,57],compar:43,complic:50,compon:[43,44,50],connect:[41,43,44],consid:43,content:38,convei:47,convert:44,correct:52,correl:[13,47],cosi:47,covari:[46,60],creat:61,curs:65,data:[0,8,27,29,30,35,36,37,65],dcsbm:52,decompos:50,decomposit:[63,70],dedic:38,defin:[47,51],degre:[41,43,44,52],densiti:43,descent:13,describ:[43,47],descript:43,detect:[0,6],diagon:44,did:61,differ:[10,44,47,61],diffus:62,dimens:[63,70],dimension:[39,65],direct:[43,52],discov:[27,35],distribut:[0,44],doe:[47,61],don:[43,58],dot:[47,50,52,55,57],down:[63,70],each:[9,11,43,50],edg:[3,43,44,47,49,50,51,52,65],eigenvector:[63,70],either:43,emb:[61,63,65,70],embed:[8,60,61,63,70],end:[26,34],equival:52,er_n:49,erdo:47,estim:[0,8,55,56,57,58,65],exampl:[18,55],exercis:[19,51],exist:[43,51],expect:52,experi:53,explicitli:50,explor:60,extrem:[63,70],fail:61,far:43,few:[63,70],figur:[38,63,70],find:[9,11,13,60,61,63,70],fine:28,first:0,flip:[53,55],foundat:52,four:61,fraction:43,from:[47,49,50,51,65],front:38,gain:[27,35],gener:[8,52],geometr:65,get:[29,36],give:44,good:13,gradient:13,graph:[13,47,50,52,55,57,59,67],graspolog:[8,13,16,60,61,63,70],grdpg:52,hand:16,has:52,have:[5,43,63,70],heck:65,help:[63,70],heterogen:61,high:65,homogen:52,how:[43,49,50,51,61,63,70],human:61,hypothesi:[0,15],identifi:61,ier:[47,52],ier_n:47,ignor:44,incid:[41,43],independ:[47,49,52],indic:43,infer:57,inhomog:47,inhomogen:52,insight:[27,35],inspir:23,invers:8,joint:[47,60,61],jrdpg:47,just:[49,50,58],known:[55,57],landscap:17,laplacian:[41,63,70],larger:43,largest:[43,44],latent:[47,50,61,65],lcc:43,learn:[16,17,20,21,26,34,54,60,61],length:43,likelihood:58,link:23,list:38,log:44,look:[25,33],loop:[43,52],loopless:[43,52],low:[44,47,63,70],lower:44,lse:[63,64,70],machin:[16,17,26,34],magnitudin:44,main:20,make:65,mani:[1,49,63,70],mase:[60,61],match:[13,67],math:39,matric:[13,39,47,50,61,63,70],matrix:[41,43,44,47,50,51,61,63,65,70],matter:38,maximum:58,method:[57,58,62,63,70],mislead:43,mle:[56,58],model:[7,28,31,45,46,47,48,50,51,52,53,55,56,57,58,60,68,69],moor:8,more:50,move:13,much:43,multipl:[14,47,61,68],network:[0,1,5,9,11,12,13,14,16,17,20,21,22,23,24,26,30,34,37,40,41,42,43,44,46,47,48,49,50,51,52,54,55,56,57,59,60,61,63,65,68,69,70],neural:59,node2vec:62,node:[9,11,43,44,49,51],nomin:[9,11,14],non:[60,61],normal:44,now:[63,70],number:[43,55,57],numer:39,object:40,often:65,omni:61,omnibu:[60,61],onli:[63,70],ordin:44,orient:41,origin:[63,70],our:[0,61,63,70],out:[8,50,52,63,70],pair:[13,55,57],paramat:51,paramet:[55,56,57],parametr:[49,50],path:43,penros:8,permut:13,pictur:[25,33],plot:[55,57],point:0,posit:[47,50,61,65],possibl:[43,49,60],posteriori:[52,55],potenti:44,prepar:[30,37],preserv:44,priori:[52,55],probabl:[8,39,49,50,51,52,65],problem:21,product:[47,50,52,55,57],project:[26,34],properti:[40,43],pseudoinvers:8,quantifi:43,quantiti:39,random:[41,47,49,50,51,52,55,57,62],rank:[44,47,63,70],rdpg:[47,50,52],rdpg_n:50,realiz:[47,49,50,51],reason:60,recap:[55,57],recreat:[63,70],reduc:44,refer:[9,11,49,60],refresh:39,regular:[44,63,70],relationship:44,remain:0,remov:44,renyi:47,represent:[41,42,54,60,61],result:66,rethink:50,rho:47,right:53,row:13,same:61,sampl:[8,15],sbm:[50,51,58],sbm_n:51,scikit:16,score:[44,47,61],seed:13,select:[7,31],self:43,separ:61,set:[9,11],share:47,should:[63,64,70],signific:[2,3,4],similar:47,simpl:[43,52,63,70],simpler:50,simul:[0,47,49,50,51],singl:[9,11,52,69],singular:[63,70],small:13,space:[39,61],sparsif:44,spectral:[9,11,57,58,60,61,63,70],spuriou:44,standard:44,state:50,statist:[0,40,43,45,53],stocahst:50,stochast:[51,52,55,56,57,60],studi:24,stuff:[63,70],subnetwork:[43,44],subset:43,subspac:47,sum:[63,70],summari:43,symmetr:[41,44],take:[44,63,70],techniqu:32,tell:[43,65],tend:43,terminolog:39,test:[0,2,3,4,10,15],than:50,thei:43,theoret:66,theori:[52,58,67,68,69],thi:[43,63,70],thing:50,think:65,thought:51,threshold:44,time:0,timeseri:0,togeth:[43,61],train:31,transform:32,triangl:44,trim:44,tune:28,two:[12,15,43,44],type:[21,22,61],understand:[63,70],undirect:[43,52],undirected:44,uniqu:47,u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