{"id":386,"date":"2013-06-01T08:43:08","date_gmt":"2013-06-01T08:43:08","guid":{"rendered":"http:\/\/larica.uniurb.it\/sigsna\/?p=386"},"modified":"2013-06-01T08:43:08","modified_gmt":"2013-06-01T08:43:08","slug":"multiple-network-models-for-complex-online-social-network-analysis-syllabus","status":"publish","type":"post","link":"https:\/\/sigsna.uniurb.it\/?p=386","title":{"rendered":"Multiple network models for complex online social network analysis – Syllabus"},"content":{"rendered":"

Matteo Magnani, Institute of Information Science and Engineering (ISTI), CNR, Italy
\nLuca Rossi, Dept. of Communication Studies and Humanities, University of Urbino, Italy
\n<\/em>
\nTutorial summary<\/em><\/strong>
\nThe last two decades have witnessed the proliferation of several Social Network Sites (SNSs).
\nWhile it is not clear whether only one or few big SNSs will survive in the near future, or multiple
\nspecialized services will still exist separately, we can claim that a model based on a single layer
\nof social connections will never be able to accurately describe our complex and layered online
\nsocial experience: while Facebook connections can explain a lot about a user\u2019s social life, his\/her
\nprofessional network may require an analysis of LinkedIn connections and his\/her information
\nconsumption practices might be better explained by looking at his\/her Twitter network.
\nDecades before the advent of SNSs a similar layered scenario had already been described by
\nsociologists like Goffman for which individuals perform on multiple stages, creating a sort of
\nfragmented public personality whose different components relate to different audiences (and thus
\nnetworks) and anthropologists like Gluckman who observed human relationships characterized
\nby their multiplexity. While this view has been widely used by early digital\u00adculture researchers, it
\nhas not been regularly applied together with Social Network Analysis (SNA) methods to study
\nonline SNSs.
\nHowever recent works have re\u00addefined the foundations of multi\u00adlayer network models highlighting
\nthe opportunity to apply SNA approaches to a wide range of complex social relationships as well
\nas study the mutual influences between different co\u00adexisting networks.
\nThis tutorial will review the main theoretical models, data gathering methods and analytical tools
\nto deal with multiple networks and to understand how a multi\u00adlayer network perspective may
\nchange our knowledge of user behaviours. Multiple online network analysis is a recent and
\ngrowing field, with long\u00adstanding theoretical bases rooted in classical sociological analysis and
\nmultiplex social network analysis methods. As such, it presents numerous research\u00a0opportunities both for experienced researchers and young academics looking for a field of
\nspecialization.
\nPrerequisites and outcomes<\/em><\/strong>
\nParticipants will benefit from a general knowledge of the basic concepts in Social Network
\nAnalysis, in particular centrality measures. However, the required concepts will be briefly recalled
\nas needed during the tutorial.
\nThe main intended learning outcomes are the following:
\n\u00ad Know the historical roots of multiple network analysis.
\n\u00ad Recognize the specific aspects emphasized by different models for multiple networks.
\n\u00ad Theorize the main socio\u00adcomputational challenges of multiple network analysis.
\n\u00ad Perform multiple network analysis tasks on real data.
\n\u00ad Identify the most promising research directions in the area.
\nDetailed course description<\/em><\/strong>
\nThe tutorial is divided into five main sections. We indicate the general topic of each section with a
\nfew selected suggested readings \u00ad a more exhaustive list will be distributed to the participants
\nduring the tutorial.
\n1) Historical foundations of Multiple Network Analysis<\/span>
\nWhile the topic of multiple network analysis has recently seen a rise in general interest, largely
\nconsequent to the new wave of interest that has been addressed to single layer networks from
\nmany different research fields, it can be rooted into a long\u00adstanding research tradition. In order to
\nintroduce the topic we will examine the early literature that in many different research areas (from
\nanthropology to sociology) considered the multiplex nature of human beings. These studies,
\nspanning several kinds of communication, have introduced the idea that it may not be
\nmethodologically correct to analyze a partial network by isolating just a specific kind of tie.
\nStarting from these premises we will show how social sciences have often considered
\nmultiplexity even out of the context of social network analysis.
\nSuggested readings:<\/em>
\nSkvoretz J and Agneessens F (2007) Reciprocity, multiplexity, and exchange: Measures. Quality
\n& quantity, Springer, 41(3).
\nMinor MJ (1983) New directions in multiplexity analysis. Applied network analysis.
\n2) Models & measures for Multiple Networks<\/span>
\nIn this section we will introduce the main models and measures. We will briefly review models
\nallowing multiple node types (also called heterogeneous or multi\u00adtype networks), models allowing
\nmultiple relationship types (also called multi\u00addimensional networks), multi\u00adslice models and
\nmodels explicitly representing the co\u00adexistence of multiple networks (also called multi\u00adlayer(ed)
\nor multi\u00adstratum networks). Then we will focus on the main measures. Here we will introduce two
\ndifferent approaches, respectively reducing multiple networks to a single traditional network and
\nkeeping the layers separate. We will define and exemplify degree and neighborhood centrality,
\ndimension relevance, multi\u00adlayer distance.
\nSuggested readings:<\/em>
\nBerlingerio M, Coscia M and Giannotti F (2011) Finding and Characterizing Communities in
\nMultidimensional Networks. In: 2011 International Conference on Advances in Social Networks
\nAnalysis and Mining, IEEE computer Society.
\nMagnani M and Rossi L (2011) The ML\u00admodel for multi\u00adlayer social networks. In: The 2011
\nInternational Conference on Advances in Social Network Analysis and Mining, Los Alamitos, CA,
\nUSA, IEEE computer Society.
\n3) Formation & Evolution of Multiple Network<\/span>
\nNetwork formation models are among the most important tools in Network Science and Social
\nNetwork Analysis. A typical application of artificially generated networks is to provide null models
\nthat can be used to test new measures and make comparisons with real networks, so that
\nsignificant patterns can be highlighted in the real data. In addition, these models are useful to test
\nhypotheses on the dynamics underlying network evolution. However, most existing generative
\nmodels have been developed to describe the evolution of single networks. In this section we will
\nreview some very recent works modelling the co\u00adevolution of multiple networks.
\nSuggested readings:<\/em>
\nB. Podobnik, D. Horvati\u0107, M. Dickison, and H. E. Stanley (2012) Preferential Attachment in the
\nInteraction between Dynamically Generated Interdependent Networks, Europhys. Lett. (EPL) 100,
\n50004
\nMagnani M and Rossi L (2013) Formation of multiple networks. In: Social Computing,
\nBehavioral\u00adCultural Modeling and Prediction, Springer.
\n4) Clustering & Community detection in Multiple Networks<\/span>
\nAlthough several community detection algorithms for single social networks exist, the discovery
\nof communities spanning multiple networks is still a largely unexplored topic. At the same time,
\nsome recent works have identified new computational approaches to tackle this complex
\nproblem. In this section we will present a selection of community detection methods for multiple
\nnetworks, highlighting the research context where they emerged and showing applications to real
\ndata.
\nSuggested readings:<\/em>
\nMucha PJ, Richardson T, Macon K, et al. (2010) Community Structure in Time\u00adDependent,
\nMultiscale, and Multiplex Networks. Science, American Association for the Advancement of
\nScience, 328(5980), 876\u2013878, Available from: http:\/\/dx.doi.org\/10.1126\/science.1184819.
\nBrigitte Boden, Stephan G\u00fcnnemann, Holger Hoffmann, Thomas Seidl (2012) Mining coherent
\nsubgraphs in multi\u00adlayer graphs with edge labels. KDD.
\n5) Multiple Network Data: Retrieval and Ethical issues<\/span>
\nThe collection of well structured multiple network data can be a difficult task. Within this last part
\nof the tutorial we are going through some of the related problems. We will also present some
\navailable multiple network datasets. At the same time we will present some thoughts on the
\npractical and ethical aspects involved in multiple network data collection.
\n 
\nBiographies<\/em><\/strong>
\nMatteo Magnani graduated in Computer Science at the University of Bologna in 2002 (110\/110
\nwith mention). He studied at the University of Marne la Vall\u00e9e (undergraduate level) and the
\nImperial College London (postgraduate research level). In 2006 he obtained a PhD in Computer
\nScience (Bologna) where in 2011 he also graduated in Violin (110\/110 with mention). He has
\nreceived a Rotary Prize for the best student of the Science Faculty (UniBO), and his mother is
\nvery proud of him (or at least this is what she officially says). Until May 2012 he was an assistant
\nprofessor (RTD) at the Dept. of Computer Science, University of Bologna and he has held a
\nposition at research assistant professor level at the Data Intensive Systems group, Dept. of
\nComputer Science, Aarhus University, Denmark. He is currently at KDD Lab, ISTI, CNR, Pisa
\n(Italy), and since August 2013 he will be Associate Professor at the Department of Computing
\nScience, Uppsala University, Sweden.
\nHis main research interests span Database and Information Management systems, specifically
\nuncertain information management and multidimensional database queries, Network Science
\nand Social Computing. He has written around 1.5 Kg of papers on these topics (when printed on
\nheavy A4 size sheets). He has several years of teaching experience and has obtained the
\nPedagogical Training Certificate at Aarhus University.
\n 
\nLuca Rossi is Assistant Professor of Media Analysis at the Department of Communication
\nStudies and Humanities, University of Urbino Carlo Bo, Italy. He works on SNA techniques
\napplied to Social Media data and to the analysis of audience practices. He presented his work in
\nmany international conferences, among others: IR, SBP, ASONAM, SunBelt, ICWSM. He has
\nteaching experience both at undergraduate level where he teaches Sociology of New Media and
\nMedia Analysis and at the graduate level where he teaches Social Network Analysis techniques
\nas a compulsory class of the PhD program in Sociology of Communication at the University of
\nUrbino. Since August 2013 he will be at IT University in Copenhagen, Denmark.
\nMatteo and Luca have a growing experiences in the field. In 2011 they won the Best Paper
\nAward at the ASONAM conference for their seminal paper \u201cThe ML\u00admodel for multi\u00adlayer social
\nnetwork analysis\u201d where they defined concepts and methods to study multi\u00adlayer online social
\nnetworks. In 2012 they organized the first International Workshop on Complex Social Network
\nAnalysis, they are organizing the Symposium on Multiple Network Analysis and Mining (satellite
\nevent of NetSci 2013) and they have authored the entry on Data Structures and methods for
\nmining multiple social networks for the upcoming Encyclopedia of Social Network Analysis and
\nMining (Springer). Putting together two different backgrounds (respectively, computational and
\nsociological) they will also be able to provide insights on opportunities and challenges of doing
\ninterdisciplinary research on these topics. Together, they have successfully attracted funding
\nfrom Working Capital (Telecom Italia), PRIN and FIRB (MIUR \u00ad Italian Ministry for education,
\nUniversity and Research) schemes.
\nContact information<\/em><\/strong>
\nMatteo Magnani: ISTI, CNR, Via Moruzzi 1, Pisa, IT. email matteo.magnani@isti.cnr.it, phone +39
\n333 3833579.
\nLuca Rossi: Dept. of Communication Studies and Humanities, Via Saffi 15, 61029 Urbino, IT.
\nemail luca.rossi@uniurb.it phone +39 0722 305726<\/p>\n","protected":false},"excerpt":{"rendered":"

Matteo Magnani, Institute of Information Science and Engineering (ISTI), CNR, Italy Luca Rossi, Dept. of Communication Studies and Humanities, University of Urbino, Italy Tutorial summary The last two decades have witnessed the proliferation of several Social Network Sites (SNSs). While it is not clear whether only one or few big SNSs will survive in the … Continua a leggere Multiple network models for complex online social network analysis – Syllabus<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2,3,4,5,1],"tags":[8,14,27,40,41,42,56,58],"class_list":["post-386","post","type-post","status-publish","format-standard","hentry","category-conferences","category-firb","category-highlights","category-impact","category-uncategorized","tag-analysis","tag-complex","tag-icswm13","tag-multiple","tag-multiplex","tag-network","tag-syllabus","tag-tutorial"],"_links":{"self":[{"href":"https:\/\/sigsna.uniurb.it\/index.php?rest_route=\/wp\/v2\/posts\/386","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sigsna.uniurb.it\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sigsna.uniurb.it\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sigsna.uniurb.it\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/sigsna.uniurb.it\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=386"}],"version-history":[{"count":0,"href":"https:\/\/sigsna.uniurb.it\/index.php?rest_route=\/wp\/v2\/posts\/386\/revisions"}],"wp:attachment":[{"href":"https:\/\/sigsna.uniurb.it\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=386"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sigsna.uniurb.it\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=386"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sigsna.uniurb.it\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=386"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}