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CSE291-F:GraphMiningandNetworkAnalysis TentativeSyllabus DepartmentofComputerScienceandEngineering,UCSanDiego,Spring2017 Instructor:FragkiskosMalliaros Email:[email protected] Office:AtkinsonHall,4111 Web:http://fragkiskos.me CourseOverview Networks (or graphs) have become ubiquitous as data from diverse disciplines can naturally be mapped to graph structures. Socialnetworks, such as academic collaboration networksandinteractionnetworksoveronlinesocialnetworkingapplicationsareusedto representandmodelthesocialtiesamongindividuals.Informationnetworks,includingthe hyperlink structure of the Web and blog networks, have become crucial mediums for information dissemination, offering an effective way to represent content and navigate through it. A plethora of technological networks, including the Internet, power grids, telephonenetworksandroadnetworksareanimportantpartofeverydaylife.Theproblem ofextractingmeaningfulinformationfromlargescalegraphdatainanefficientandeffective way has become crucial and challenging with several important applications and towards thisend,graphminingandanalysismethodsconstituteprominenttools. Thegoalofthiscourseistopresentrecentandstate-of-the-artmethodsandalgorithmsfor exploring,analyzingandmininglarge-scalenetworks,aswellastheirpracticalapplications invariousdomains(e.g.,socialscience,theweb,biology). LearningObjectives This course aims to introduce students to the field of mining social and information networksby(i)coveringawiderangeoftopics,methodologiesandrelatedapplications;(ii) giving the students the opportunity to obtain hands-on experience on dealing with graph dataandgraphminingtasksthroughexercisesandprojects. Weexpectthatbytheendofthecourse,thestudentswillhaveathoroughunderstandingof various graph mining and learning tasks, will be able to analyze large-scale graph data as wellastoformulateandsolveproblemsthatinvolvegraphstructures. Prerequisites Thereisnoofficialprerequisiteforthiscourse.However,thestudentsareexpectedto:(i) havebasicknowledgeofgraphtheoryandlinearalgebra;(ii)befamiliarwithfundamental data mining and machine learning tasks; (iii) be familiar with at least one programming language(e.g.,Pythonoranylanguageoftheirpreference). CourseSchedule(Tentative) Week Topics 1 • • Introductionandoverviewofgraphmining Graphtheoryandlinearalgebrarecap 2 • Real-worldnetworkproperties(nodedegreedistribution,shortest paths,clusteringcoefficient,smallworldphenomenon,propertiesof timeevolvinggraphs) Networkgenerativemodels(randomgraphs,preferential attachmentmodel,Kroneckergraphs,stochasticblockmodels) Randomwalks Graphcentrality Linkanalysisalgorithms(PageRankandHITS) Graphclusteringandcommunitydetection(spectralclustering, graphpartitioning,modularity-basedalgorithms,community structureofreal-worldgraphs,overlappingcommunities) Nodesimilarity Linkprediction Graphkernelsandgraphsimilarity Graphclassification Learningembeddingsingraphs Applicationsinnodeclassificationandlinkprediction Influentialspreaders Influencemaximization Topicsingraphminingandapplications Densesubgraphdetection Richnetworkstructures(signedandmultilayernetworks)and applications Anomalydetection Geo-socialandlocationbasednetworks Projectpresentations • 3 4 5 6 7 8 9 10 • • • • • • • • • • • • • • • • • • Evaluation(Tentative) Theevaluationofthecoursewillbebasedonthefollowing: 1. Hands-on practical assignments: the hands-on assignments (two assignments) will familiarize the students with basic graph mining and analysis tasks (e.g., analysis of a social or information network such as the DBLP co-authorship network;crawlthedataandconstructthegraph;examinethestructuralproperties ofthegraph;findtheunderlyingcommunities). 2. Researchproject:thiswillbethemaincomponentfortheevaluationofthecourse. The students are expected to form groups of 3-4 people, propose a topic for their project,andfinally,presenttheprojectattheendoftheclass(eitherinclassorin postersessionthatwewillorganize). ReadingMaterial Mostofthematerialofthecourseisbasedonresearcharticles.Someofthetopicsarealso coveredbythefollowingbooks(allofthemarepubliclyavailable): • DavidEasleyandJonKleinberg.Networks,Crowds,andMarkets.Cambridge UniversityPress,2010. • DeepayanChakrabartiandChristosFaloutsos.GraphMining:Laws,Tools,andCase Studies.SynthesisLecturesonDataMiningandKnowledgeDiscovery,Morgan& ClaypoolPublishers,2012. • Albert-LászlóBarabási.NetworkScience.CambridgeUniversityPress,2016.