Welcome to Social Network Analysis.My name is Lada Adamic I'm an associateprofessor at the University of Michigan.I'm affiliated with the School ofInformation, The Center for the Study ofComplex Systems and the Computer ScienceDepartment.What I'd like to show you in this courseis what we can get out of modeling theworld around us as networks.Now the world is very complex and once yourepresent it as a network,It may not really look any less complex,But indeed, we can gain very usefulinsights.We can start to understand how informationdiffuses in social networks.We can also understand how resilientdifferent infrastructure networks, such asroads, or the electrical power grid are,to random or intentional builders.Here's one example I'd like to start with.These are hand drawn networks made by theartist Mark Lombardi.He constructed them by pouring over newsarticles in the 1980's and 1990's,Making connections between politicalentities and different financialinstitutions and corporations.When you leave them out, you could seeconnections that might not otherwise beobvious just by reading the news articlesone by one.Here's Michael Kimmelman a columnist forthe New York Times commenting on havingencounters a few folks from the Departmentof Homeland Security at an exhibit of MarkLombardi's art.They found the work revelatory,Not because the financial and politicalconnections he mapped were new to them,But because Lombardi showed them anelegant way to array this spreadinformation, and make sense of things,Which they thought might be useful totheir security efforts.Now, in this class I'm not going to makeyou do hand-drawn network layouts, eventhough there really hard to beat, but MarkLombardi would spend days drawing theseover and over until they were perfect.Instead, what we are going to be doing isusing automated layout algorithms, insoftware such as Gephi.Now, here is a nice example of howautomated layout algorithms make thingsvery apparent.All they're doing is placing nodes thatare connected through edges close togetherand, other nodes are repelled.

Actually, all nodes experience areflection force so that they're not allclumped together unless the ties bringthem together.This is a data set of political blogsprior to the 2004 presidential electionand this is who follows who,Who, who has whom on their blog roll.The Liberals are colored in blue, theLiberal blogs.The Conservatives are colored red.Liberal to Liberal ties are blue,Conservative to Conservative are red.Liberal to Conservative are purple,Conservative to Liberal, orangish yellow.And what is apparent right away is that tosome extent, there's an echo chambereffect,Where Liberals are primarily talking toLiberals and Conservatives are primarilytalking to Conservatives,And all I really had to do was run onelayer algorithm.I did not have, in this case, although wedid do this in our study,To even do any calculations for thispattern to be to be apparent.Now here is another example of the dataset I have gathered, This is anorganization of Hewlett-Packard labs, so abunch of researchers,And what we looked at were, was theree-mail communication.If two people had exchanged at least acouple of emails back and forth over theperiod of a few months they get a grayetch.I've overlaid here black edges, whichrepresent the formal organization, whoreports to whom.Now, what is immediately apparent fromthis visualization, and what we confirmedin the study,Is that the email communication is morelikely to occur between individuals whoare closer together in the organizationalhierarchy.But there are enough shortcuts across theorganization that any two individuals areconnected through a short number of hops.But the fact that those hops roughlyfollow the organizational hierarchy makesthe e-mail network navigable.So informal collaboration getting the jobdone, it's reflected in this network.This network is my Facebook network andwhat I've done here is I've used anautomated community detection algorithm,in addition to an automated layoutalgorithm [laugh] to layout my Facebook

friends.And what the automated community detectionalgorithm did was it said, oh, there seemto be some people in your network who are,tied together and" more so, they'reconnected more than they are to the restof your network.And indeed, once you're working with thisdata, within an automized version of thisdata, you'll see that the different groupsroughly corresponds to different contextin, which I've met people from school towork, to outside of school and workactivities.And this you can just tell without, youknow, without really knowing anythingabout my life, you can look at my networkand, and understand quite a bit of it.The final network is one of ingredients,recipe ingredients.We analyzed tens of thousands of recipesto figure out which ingredients go welltogether and then we made a network.In fact, we made several networks and inthis one you can see that there are twomain communities. One of savorieringredients, and one of sweeteringredients.And actually, at the very top, there's asmaller community, and you'll, you'll playwith this data in time.That is the mixed drink community, whereyou have ingredients such as vodka and,and lime juice.So, what I've shown you so far is that,you know, even just visualization can buyyou a lot in understanding what the myriadof connections that we know are there,Can, can represent,But that might be rather invisible to usuntill we represent them as this networkwhere they're all connected together andthis is where we're going to get the niceinsight.I am going to be using maybe somewhatinconsistent terminology, so I mayalternate between the words network andgraph.Graph is the terminology where, you know,it all started in the field of mathematic,but I'm more likely to use the wordnetwork for example the new emerging fieldnow is network science and it doesn't makea network any less of a graph, it's justthat you can use both terms.Similarly, I'm going to primarily use thewords nodes." and esges,However, nodes can also be referred to asvertices. if you're talking, bboutsociological phenomenon.

Are you talking to a sociologist?They might use the word, actor.Similarly, for ties, a sociologist mightsay, ties, [laugh] or relations.A physicist might talk about sites andbonds, although physicists who work onnetworks do say, nodes and edges.And finally, in computer science, youmight be talking about links, especiallyif you're talking about networks, such asthe World Wide Web.So we have a variety of terminology, it's,it's very easy to, get used to it, and allwe're talking about in the end is that youhave different entities and theconnections between those entities, andthat is what we're going to analyze.Let me get to the goals in this course.In addition to making pretty pictures weneed to really understand what thestructure of a network is.So we're going to do some measurement andin this measurement, we're going to lookat whether nodes are connected through thenetwork.We're going to look at how far apart theyare in the network.How many hops does it take following thesedifferent connections?We're going to look at whether some nodesare more important than others due totheir position in the network.And we're going to look at whether thereare these communities in the network, thatis, sets of nodes that are especiallydensely connected.We are not going to be satisfied justknowing that there's this structure.We want to know where does this structurecome from?What kinds of processes shape a network?So we're going to start with randomlygenerated networks, where you're justthrowing edges at random and connectingdifferent minutes.Then we're going to look at preferentialattachment where it's a phenomenon of richget richer.As new edges are added, they're morelikely to be added to the nodes that arealready popular, in a sense.They already have many other edges.We're going to be looking at small worldnetworks as well so you might haveprocesses, such as a friend of a friend islikely to be a friend because friends tendto introduce their friends to each other.And yet any two people in the world areconnected through a short number of hops.So recently a Facebook study showed that

any two people in the Facebook graph areconnected with an average of 4.7 hops.We're going to see how certain processesmight shape such small-world structure,for example, how do small worlds arise outof optimization, for example, airlinenetworks might be optimized to ferrypassengers back and forth in a way that isefficient and doesn't cost the airlinesmuch money.You might also have strategic networkformation at the level of the individual,so the individual is getting something outof participating in the network.And so they may choose to connect to somenodes and not others.Okay.So,We're going, we've described the networkstructure.We figured out where that networkstructure comes from.And the final goal is to understand howthat network structure now influencesdifferent processes occurring on thenetworking [inaudible].So for example, we're going to learn howinformation diffusion is affected by thenetwork structure.If any two people are, are connectedthrough a short number of pops does thismean that information will readilydiffuse.Sometimes it's not information that'sdiffusing but something that we don't wantto diffuse such as a virus.So, how does the social network actuallyinfluence how quickly a virus is going tospread, and what immunization strategiescan you use once you know what thestructure of the network is?We may study the process, or we are goingto study processes such as opinionformation.This can be, kind of a consensus, that canbe reached across the network asindividuals continuously update theirbeliefs or it may be just a single shot,You, you, you form your opinion only once,But it is influenced by what your friendsthink.We're also going to be looking atcoordination and cooperation.If you have a certain task that you'd liketo do, but it,But it depends on increments from thenodes that you're tied to, how quickly canyou accomplish the task?And finally, we're going to look atresilience to attack, so if for some

reason, a certain, subset of the nodes,are removed from the network, can thenetwork still function?Now, I filled in the first six weeks, soyou might be wondering what we're going tobe doing in weeks seven and eight.In week seven, we're going to look at cooland unusual applications of social networkanalysis.We're going to be looking at things suchas recipe ingredient networks forpredicting recipe ratings.We're going to be looking at the socialnetworks of dolphins and, we're also goingto look at, economic development.So if you have the network of countriesand the products that they produce, canyou actually make predictions about whichproducts the countries are going toproduce in the future and how rapidlythose countries are going to developeconomically.In the final week, we're going to look athow a social network analysis is used bycompanies such as Google, Facebook,Linkedin, Twitter, CouchSurfing, toenhance their product offerings.So what kind of social network analysis dothey do?What kinds of research have they done?And, how has this impacted and, benefited,the,The features of the social networks thatthey enable?So those are that's the outline of thecourse.In the next video I'm going to dive rightinto it and we are going to visualizesome,Some networks and see what it's all about.