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Detecting Patterns in Complex Social Networks 167

Roland Piquepaille writes "So-called social networking is very popular these days, as show the proliferation of services like Friendster, Orkut and dozens of others. But do the companies behind these services have any idea of what is hidden inside their complicated networks? When these networks reach a size of millions of users, it's not an easy task. A researcher at the University of Michigan is trying to help, with a new method for uncovering patterns in complicated networks, from football conferences to food webs. This overview contains more details and references about this non-traditional method. It also includes a spectacular representation of the Internet and another image showing a food web at Little Rock Lake."
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Detecting Patterns in Complex Social Networks

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  • by mekkab ( 133181 ) * on Monday February 16, 2004 @02:27PM (#8295957) Homepage Journal
    I don't want to downplay the possible significance, but if you are focusing on the "clumps" (what disparate entities have in common) isn't this akin to taking slices out of a data warehouse? Aligning everything along a single axis?

    • by enkindle_networks ( 753282 ) on Monday February 16, 2004 @05:26PM (#8297867)
      The results have been known by social networks researchers long ago. They are being "discovered" by physicists, complex system scientists, and computer scientists.
      What is interesting actually is NOT the clumps (the paper is wrong), but the (possibly heterogeneous, multi-modal and dynamic) networks and their various measurements that could reveal lots of things.
      The parent is right in pointing a possible method of extracting the results, but ignores how one constructs the data warehouse in the first place and the significance of networks -- especially the social and dynamic ones -- instead of data warehouse, both of which are not trivial problems.
      Several websites may enlighten those who are interested in probing social networks deeper:
      INSNA is the professional association for researchers interested in social network analysis.
      CASOS brings together computer science, dynamic network analysis and the empirical study of complex socio-technical systems. Computational and social network techniques are combined to develop a better understanding of the fundamental principles of organizing, coordinating, managing and destabilizing systems of intelligent adaptive agents (human and artificial) engaged in real tasks at the team, organizational or social level. Whether the research involves the development of metrics, theories, computer simulations, toolkits, or new data analysis techniques advances in computer science are combined with a deep understanding of the underlying cognitive, social, political, business and policy issues.
      The Journal of Social Structure (JoSS) is an electronic journal of the International Network for Social Network Analysis (INSNA). It is designed to facilitate timely dissemination of state-of-the-art results in the interdisciplinary research area of social structure. It publishes empirical, theoretical and methodological articles.

      JoSS publishes manuscripts that are focused on social structure-on the patterning of social linkages among actors. These actors could be comprised of different types or levels or analysis, such as animals, humans, artificial agents, groups or organizations. INSNA was founded on the premise that the behavior and lives of social entities are affected by their position in the overall social structure. By examining the etiology and consequences of structural forms overall, of the location of entities within these structures, and of the formation and dynamics of ties that make up these structures, INSNA hopes to learn about the parts of behavior that are uniquely social. L&_cdi=5969&_auth=y&_acct=C000050221&_version=1&_u rlVersion=0&_userid=10&md5=0dbd43b8d4784bc1532be7b 6c056be81
      Publication of social networks papers.
    • I think I might not have understood your question or objection, but, looking for the clumps is a mathematical approach tightly connected to SVD (Singlular Value Decomposition) of the data warehouse (matrix).

      The reason to do so in the first place is because the data's pattern or structure is way too complex for us to see (since it's only visible in high-dimensionality). Rather, we can calculate groups with linear algebra and then extract those groups and make a visualization out of them.

      This is roughly
  • I think if the internet was studied as a social network, on might find that someone like Janet Jackson was the core of society :-)

  • Slashdot?..... (Score:5, Insightful)

    by BWJones ( 18351 ) * on Monday February 16, 2004 @02:29PM (#8295981) Homepage Journal
    But do the companies behind these services have any idea of what is hidden inside their complicated networks?

    I have often wondered this about Slashdot itself. It would appear to me that Slashdot would provide an ideal means to mine data on complex interactions that may have implications for anything from database design to network load analysis or perhaps the results may even apply to the modeling of biological systems. The owners of Slashdot would be missing something big if they were not examining Slashdot very carefully.

    Mapping the Internet only has so many applications, but if one really wanted to make an obscene amount of money, figuring out how to model systems is where it would be.

    • by Anonymous Coward on Monday February 16, 2004 @02:33PM (#8296019)
      Obviously they are doing this, I mean that is why we get such shoddy editing sometimes, like duplicate posts and the like. They are trying to discover the meaning to life and the universe by studying /.

      If only they would read some of the comments and realize that the answer is 42, they might be able to work faster backwards from the answer.
    • Re:Slashdot?..... (Score:5, Interesting)

      by kfg ( 145172 ) on Monday February 16, 2004 @02:43PM (#8296139)
      On the other hand, if one is interested in science. . .

      I'd be more interested in seeing the data that gets deleted, not the clumps. This isn't to say that the clumps aren't important, especially if you're trying to rebuild oyster populations in the Chesepeake or some such, but plenty of people will be focusing on those. People have an attraction to like objects and group mechanism.

      I have an attraction to the exceptions. That's where the really interesting scientific stuff is likely to be happening, and where the Nobels are most likely to be hiding.

      Why is this star off the main sequence? How did it get there, what makes it tick? What relevance might that have to stars that are on the main sequence?

      • Re:Slashdot?..... (Score:5, Interesting)

        by BWJones ( 18351 ) * on Monday February 16, 2004 @02:51PM (#8296232) Homepage Journal
        I'd be more interested in seeing the data that gets deleted, not the clumps.

        Following data clumping, it's really the interactions or the nexus of contact that is interesting. For instance, from a computer science or informational processing perspective, what draws someone to a piece of information? How does one direct information to be most useful? In biological systems, the nexus points are where life happens. For instance, the small molecular fluxes that are constantly providing for molecular signaling, protein synthesis etc.... Information is not lost per se, rather there are information fluxes.

        So, to answer your question of stars, it could simply be that a particular star is off the main sequence because of earlier smaller phenomenon that resulted in its appearance much later off the main sequence. Alterations in gravity? Interactions with a binary star? Alterations of proton-proton chains?

        • Re:Slashdot?..... (Score:3, Interesting)

          by kfg ( 145172 )
          All of which may well lead us to an unexpected physical phenomenon, which in turn leads us to greater understanding of star formation and evolution, perhaps even greater understanding of matter itself.

          In the biological field we may discover Black Smokers, where we learn more about life in general than we do when studying oysters and their ecologies.

          It's simply my preference to overtly assume something like Black Smokers are out there somewhere and go looking for them.

          In social networks you'll often find
    • I had the same thought some years ago and implemented a similar concept in memigo []. Memigo is a newsbot/news aggregator where users can rate articles; memigo is also aware of the context of each article, so implicitly users rate contexts as well. Based on that data, some neat things are doable: Amazon-like collaborative filtering, automated formation of "alike" users, "Interest Alerts", where the newsbot searches for articles with context that you have rated highly in the past, etc. Memigo is pretty exper
  • A researcher at the University of Michigan is trying to help who?
  • by Bobdoer ( 727516 ) on Monday February 16, 2004 @02:30PM (#8295994) Homepage Journal
    I wonder if this will improve search results? All the fake porn sites will be lumped together, thus, hopefully, taking them out of regular, useful searches.
  • by joeldg ( 518249 ) on Monday February 16, 2004 @02:31PM (#8296001) Homepage
    In this image.. rks/schoo l.gif

    The little single dots on the left..
    you have to feel bad for them..

    and all the "fringe" people.. they are visibly shown on the fringe..

    kind of interesting..
  • by DecimalThree ( 524862 ) on Monday February 16, 2004 @02:35PM (#8296041)
    We see and understand patterns based on the amount of data we can digest (which has gone much further with computers). Knowing that you could always be one data set off defining a pattern makes you wonder if chaos exists at all, hence the replacement of words like chaos with words like "complex".
  • by PureFiction ( 10256 ) on Monday February 16, 2004 @02:36PM (#8296058)
    From football conferences to food webs: U-M researcher uncovers patterns in complicated networks
    SEATTLE---The world is full of complicated networks that scientists would like to better understand---human social systems, for example, or food webs in nature. But discerning patterns of organization in such vast, complex systems is no easy task.

    "The structure of those networks can tell you quite a lot about how the systems work, but they're far too big to analyze by just putting dots on a piece of paper and drawing lines to connect them," said Mark Newman, an assistant professor of physics and complex systems at the University of Michigan.

    One challenge in making sense of a large network is finding clumps---or communities---of members that have something in common, such as Web pages that are all about the same topic, people that socialize together or animals that eat the same kind of food. Newman and collaborator Michelle Girvan, a postdoctoral fellow at the Santa Fe Institute in Santa Fe, New Mexico, have developed a new method for finding communities that reveals a lot about the structure of large, complex networks. Newman will discuss the method and its applications Feb. 15 at the annual meeting of the American Association for the Advancement of Science in Seattle.

    "The way most people have approached the problem is to look for the clumps themselves---to look for things that are joined together strongly," said Newman. "We decided to approach it from the other end," by searching out and then eliminating the links that join clumps together. "When we remove those from the network, what we're left with is the clumps."

    The researchers tested their method on several networks for which the structure was already known---college football conferences, for example. In college football, teams in the same conference face off more frequently than teams in different conferences. When inter-conference games do occur, they're more likely to be between teams that are geographically close together than between teams that are far apart. Plugging in information on frequency of games between pairs of teams in the 2000 regular season, Newman and Girvan tested their method to see if it could correctly sort the colleges into conferences. "There were a few cases where it made mistakes, but it got well over 90 percent of them right," said Newman. "It gave us the structure we were expecting, so that was encouraging."

    Newman and Girvan---and other researchers who've learned about their work---have gone on to apply the technique to systems where the structure is not as well understood, looking at everything from networks of Spanish language web logs to communities of early jazz musicians to a food web of marine organisms living in Chesapeake Bay.

    "Networks and other systems that we study are becoming increasingly large and complicated these days," said Newman. "New methods like this help us to make sense of what we see and to understand better how things work."


    For more information:
    Mark Newman --
    American Association for the Advancement of Science --
    Santa Fe Institute --

  • Orkut is very insecure.
    I heard of account deletion because of faked/spoofed "delete my account" mails.

    Remember to check their Terms [] :
    By submitting, posting or displaying any Materials on or through the service, you automatically grant to us a worldwide, non-exclusive, sublicenseable, transferable, royalty-free, perpetual, irrevocable right to copy, distribute, create derivative works of, publicly perform and display such Materials.

    They invented their own licencse. What do you think if Micro []

  • by superpulpsicle ( 533373 ) on Monday February 16, 2004 @02:38PM (#8296074)
    I know someone with 400 friends in his network on friendster. Yet he strongly claims to have never talked to any of these people. How in the world did these people end up on his list?

    There should be some kind of requirements forcing you to somewhat communicate with these people, otherwise they should be off your list.

    These social networks are giving "friends" a real bad definition.
    • by PureFiction ( 10256 ) on Monday February 16, 2004 @02:47PM (#8296181)
      The problem is more complicated, and you touch on one of the main weaknesses of any system where reputation and feedback in involved.

      One aspect of the problem is the granularity by which relationships are defined. In many of the sites there is only one state: "friend or non friend". The real world encompases a number of shades and types, from business acquaintance to personal friend, intimate lover, etc.

      Another aspect is the incentive to "game" these systems by increasing your friend count. This inevitably leads people loosening their interpretation such that they increase their visibile friend count. If the number if friends you were linked to was not public, there would be less of this (but you can't do that without breaking some of the functionality of the sites)

      People have talked about "winning" at friendster or tribe or orkut - but there is no "winning" in these systems, as there should not be competition.

      Last, there is no method for verification of any status between peers. Can you "prove" that so and so is really a friend?

      There are others, but these are the main three, and not likely to be solved or addressed any time soon.
      • Some people have loose ideas upon which they base a friendship. How frequently have you heard someone say something like "I made a new friend yesterday". I for one find that friendships take much longer to cultivate than a day, a week, a month. It would make more sense to say "I formed the foundation of what may become a friendship yesterday". As subjective as it is in life, I don't see how one could programmatically prove friendship in any way, aside from taking the word of both parties who claim frien
        • freindship (Score:2, Informative)

          and then you get the screwed up freaks who's main focus as far as freindship goes is 'a person who is not actively trying to kill or harm me in any way'. yes. that was my definition once upon a time. and i heard it echoed later on in a couple of places independant of my home town, once i found the internet. surely, the internet has changed the entire dynamics of freindship, as i thought that most of the people out there were totally against me, when in reality, they were just trying to save themselves fac
      • I'm on, I live in Ottawa, Ontario, Canada, and 99% of my "friends" are in California.

        I certainly hope I can call a few of them to go out when/if I visit that State... ;-)

      • incentive to "game" these systems by increasing your friend count.

        This phenomena is not something new to online communities.

        Politicians and salesmen have tried to "game" social networks for millenia.

        And if you really want to study some interesting social networks, consider Multi Level Marketing (MLM) schemes which are often replete with zealous, almost religious fervor about Success and rely upon social networks for growth and an occassional well-placed meme, such as "Women Gifting Women".

        Where it w

    • by Short Circuit ( 52384 ) <> on Monday February 16, 2004 @02:55PM (#8296265) Homepage Journal
      Well, on Slashdot, I get fans because people see and like what I post. (Except for one guy, I think he's just trying to max out his friends list.) I set friends based on whether I like and appreciate what they say, and would like to be reminded that I have them set as "friends" whenever they say something I don't necessarily agree with. It helps me consider other points of view.

      Granted, its a set of small steps towards understanding the opposing point of view, but it does help broaden my horizons.

      It's actually a very useful system.
  • by ThomK ( 194273 ) on Monday February 16, 2004 @02:38PM (#8296084) Homepage Journal
    The blue node (left center) in this [] diagram was gettin' some action!
    • Although it is difficult to tell how attractive all those pink nodes are..
    • by Anonymous Coward
      How about the group in the mid right side of the loop? Pink on pink action!!
    • Now I am a rather liberal kind of person, and have absolutely no problems with different kinds of relationships. But you really have to wonder about the blue-connected-to-blue-connected-to-pink in the top left-hand corner...
    • I think that's funny, but there are some interesting points.

      If we presume that pink are girls and blue are boys, there is one poor dude to the right of that guy that looks like he took 3 of the others castoffs.

      I see only 1 homosexual relationship in the upper left corner, despite the chestnut that upwards of 10% of high school students are gay.

      It would be interesting to plot this 3-dimensionally, to set them in a z-axis chronologically, and/or weight the connections for the duration of the relationship.
  • by Anonymous Coward on Monday February 16, 2004 @02:38PM (#8296085)
    Social network analysis has been around for years in social science, so I don't see what is new here. And before anyone complains, yes, these nice pictures are also far from new.
    • Social network analysis has indeed been around for a long time. What makes Newman's work new is that he is applying new algorithms to make sense of large networks. In this particular case, he has devised a new way to divide up a network into subcommunities (see this paper [] for details [or many of these [] for lots more network analysis).

      Newman, Watts, Barabasi and others are trying to understand the nature of these types of networks (and other types), rather than just the content of the networks (orkut, slas
  • denominator (Score:4, Interesting)

    by rodentia ( 102779 ) on Monday February 16, 2004 @02:39PM (#8296088)

    The denominator in these equations should be the peer pressure quotient: the desire of most people to be like most other people.
  • by TopShelf ( 92521 ) on Monday February 16, 2004 @02:40PM (#8296106) Homepage Journal
    The researchers tested their method on several networks for which the structure was already known---college football conferences, for example. In college football, teams in the same conference face off more frequently than teams in different conferences. When inter-conference games do occur, they're more likely to be between teams that are geographically close together than between teams that are far apart. Plugging in information on frequency of games between pairs of teams in the 2000 regular season, Newman and Girvan tested their method to see if it could correctly sort the colleges into conferences. "There were a few cases where it made mistakes, but it got well over 90 percent of them right," said Newman. "It gave us the structure we were expecting, so that was encouraging."

    Finally, something that can help me understand the divisions in the NHL. I've been confused ever since they got rid of Smythe, Norris, and all the rest...
  • by torpor ( 458 ) <<ibisum> <at> <>> on Monday February 16, 2004 @02:41PM (#8296117) Homepage Journal
    The uses for this software are astounding. It is, essentially, a breed of software designed to recognize and manipulate social class systems.

    Imagine a system which tells you, easily enough, who the 'most popular person for subject ___Y___' is, in your neighborhood? Target a campaign of computer-buying to only -3- folks in an area, and end up blanketing the entire region with tuber-like memes...

    PR agencies could use this data to identify the core 'gossip leaders', the ones who have massive impact on multiple peers, and then they could target only those people with their campaigns ... imagine that ... a means of actually targetting campaigns and capers directly to the primary delivery mechanisms of word of mouth among a large group. This software can give you that.

    There are numerous religious theories, also, on the strengths of individuals and groups and the effect that these social connections have on a movement ... put this in the hands of the right (wrong?) people, and we could see social revolutions targetted and executed with such blinding accuracy and predictability that most of us simply won't know what hit us ...

    This is the danger zone. The moment we start using computers to do qualitative analysis of social dynamics, and then using the data for commercial/religious/nefarious purposes, well ... maybe its time to unplug.

    • Being socially disenfranchised is looking more attractive all the time.
    • by AoT ( 107216 )
      Thia is all pretty standard in marketing now; give the popular kids a new toy and then watch all the other kids want it. It really doesn't take a computer to figure out who everyone thinks is popular, it kind of what we humans do.

      And we are already in the danger zone, you really think the big advertisers have been ignoring this kind of thing?
      • The process used to discover trend leaders among children is pretty simple:

        1. Pick a random kid at a school.
        2. Ask them who the coolest person is that they know.
        3. Go to that kid and repeat the process.
        4. When you find a kid who responds that they are the coolest person they know, you've found your trend leader.

        Give them a toy/CD/etc. for free, and you've seeded your viral marketing campaign.
    • or imagine if they used it to predict the stock market.
    • The Foundation stories were great. Read 'em.
  • Phone Book Network (Score:5, Interesting)

    by dimss ( 457848 ) on Monday February 16, 2004 @02:44PM (#8296149) Homepage
    I have an idea. Phone books of mobile phones form another kind of network. Imagine, A has number of B in his/her phone book. B has number of C. E knows both A and B. Chances are, most of GSM users in Latvia are nodes of this network. But this network can be fragmented as well. I think we could study interesting things about society this way.

    We have 7-digit phone numbers and two mobile networks here in Latvia. Data can be stored this way:

    6787026 -> 9131415
    9131415 -> 5956564

    All we need is one hashtable (or MySQL table) and data collection interface :)
    • by Coulson ( 146956 )
      Given that your contacts are stored on your cellphone, who's to say they couldn't (or aren't currently) do this right now? I don't recall my cellphone contract saying anything specifically about them not collecting this data, so...
  • Wow (Score:3, Funny)

    by SpaceRook ( 630389 ) on Monday February 16, 2004 @02:47PM (#8296192)
    I didn't know Jackson Pollack designed the internet.
  • by rqqrtnb ( 753156 ) on Monday February 16, 2004 @02:49PM (#8296212)
    I wonder whether they'll finally be able to (dis)prove the hypothesis that everybody knows everybody else within six (or however many) degrees of separation.

    Then again, most people will probably have a connection to Nigeria due to the certain organ-lengthening drug that they are so famous for.
    • I wonder whether they'll finally be able to (dis)prove the hypothesis that everybody knows everybody else within six (or however many) degrees of separation.

      I have heard about it too. In fact, there are two persons between me and Vladimir Putin or Bill Clinton, and only three persons between me and Monica Levinsky...

    • I wonder whether they'll finally be able to (dis)prove the hypothesis that everybody knows everybody else within six (or however many) degrees of separation.

      This was first proposed in 1967 by social psychologist Stanley Milgram [], (in)famous for his shocking experiments [] on human obedience, which inspired Peter Gabriel [] to create the subversive sing-along "We Do What We're Told []", a.k.a. "Milgram's 37 []".

      This paragraph brought you by a flock of hyperlinking free associators with Erds number [] 4.
  • by Anonymous Coward on Monday February 16, 2004 @02:49PM (#8296214)
    I remember the first maps of the Internet showed that certain nodes concentrate power in terms of the number of connections they make. Google, perhaps.

    A quick reading on Zipf's Law [] shows that many natural systems (and many artificial ones that obey similar laws of construction and equilibrium) observe 'power rules' where the distribution of power is inverse to the number of entities at any level.

    Surprising that earthquakes, cities, businesses, follow the same rules. And yet quite meaningless in any direct sense because we can't manipulate these rules, only observe them.

    Human social networks also follow rules that I suspect are quite simple and possibly similar to Zipf's Law. For instance, a person can only maintain a finite number of contacts (technology may increase this number but it remains finite at any given time). Any new contact coming in displaces an existing contact. So a single person's contact list will follow a power law: twice as many contacts used half as often, ten times as many contacts used a tenth as often...

    Mapping a contact network would need to take the importance of each contact into account. I may have my grandmother in my list, but I speak to her once a year. My accountant - every week. My wife - twice a day. My girlfriend - every hour.

    Next: the differences between individuals in terms of how much time/skill they invest in networking. Gender differences... women do this much more and better than men, in general. Age differences... younger men do it less well than older men. Wealth differences... richer people do more networking, I'd suspect, until a certain point when they start to delegate it. Very poor people do very little networking.

    So, the network is not a flat map. It's got two dimensions for the lines, but each line has a thickness, and each node (individual) has a size.

    Finally, I'd suspect that the network also maps power in terms of social success. Those people with the most powerful networks (a recursive definition: the networks which involve the most powerful people) will also be the most successful socially / financially.

    But they may not be the happiest.
  • by G4from128k ( 686170 ) on Monday February 16, 2004 @02:51PM (#8296231)
    Large scale networks have limitations because real relationships are complex. The notion of A-is-a-friend-of-B or A-trusts-B is too simplistic for large scale networks. These connectivity relationships are not transitive in real-life (A-trusts-B & B-trusts-C does not imply A-trusts-C)

    Rather, the network needs some form of role-based assertion or qualification of the relationship. I know friends that I like to go hiking with, but that I disagree with politically. I know people that I do trust to recommend software, but don't trust to recommend a restaurant. And if I trust person B to recommend software, I would probably only trust that person B to recommend another person C in a limited set of domains (like software or technical issues). Thus the real relationship is more like person-A-trusts-person-B-for-role-C.

    Such a scheme of role-defined relationships could be self-organizing or predefined. The self-organizing approach would look for disjoint clusters of members in a network or use semantic analysis of the messages passed between people to infer a set of role-clusters. Predefined relationship might be OK, but could become unwieldly if the network creators force people to answer a long multiple-choice test about every relationship.
    • by PureFiction ( 10256 ) on Monday February 16, 2004 @03:20PM (#8296524)
      Rather, the network needs some form of role-based assertion or qualification of the relationship.

      The problem with this is the classic meta-data problem: how do you get users to enter in a sufficient amount of meaninful information about their peers?

      The simple approach (and also the most innaccurate/flawed) is the binary status of "friend / non-friend" which has the drawbacks you mention.

      But a much more detailed and expressive syntax would be incredibly cumbersome. For every person in your social network you would need to answer the detailed questionaire: "is this person a friend acquaintance. Is the friendship activity based, personal, business, etc." ad infinitum.

      And unless everyone responded with completeness, the validity of any given link expressed between two people could vary greatly.

      I'm a big fan of the implicit approach, and the research mentioned above goes a little ways towards implictly identifying and categorizing the nature of links between peers in a social network.

      If a system could observe your interactions with others via email, phone, web communities, etc. (and preserve the privacy of such information - but thats another discussion) then the need for explicitly defining this social metadata would be reduced, as many of the aspects of social interactions could be inferred implicitly without bothering the user to enter (partial) information themselves.

      There is a lot of progress to be made in this space; hopefully it will happen soon :-)
    • I wonder if this could be resolved by the use of rated interests, I.E. on your friendster account you not only put in your interests you also rate your trust in your friends' interests. This would allow you to easily find, for example, the most trusted computer expert in your network or maybe the opposite, the least trusted or knowledgable in a subject. That could have some nasty repercussions.
  • Hmm... (Score:5, Insightful)

    by smoondog ( 85133 ) on Monday February 16, 2004 @02:55PM (#8296266)
    Why is it that many /.'ers are so concerned with their privacy in some cases, such as pay pal, ebay, etc, and yet have no problem giving another company their contact info and the contact info of everyone they know? It seems that digitizing social networks (ala friendster) really opens you up for privacy abuse. These companies could, frankly, really mess up your life if they decided to do such a thing, or if a hacker broke in and did such a thing.
    • These companies could, frankly, really mess up your life if they decided to do such a thing, or if a hacker broke in and did such a thing.

      Crackers and malicious companies are, frankly, the very least of your worries. Bush and Ashcroft are already busily data mining commercial databases, airline records and the rest. They do this because they cannot gather much of this information themselves either for financial legal technical or political reasons.

      But Orkut Friendster et. al. even if they havent alrea
      • and then the black helicopters came swooping down after cross referencing my purchase of a BudLight with my 4th grade book report on Animal Farm.

        seriously. the social networking efforts are going to be no worse than people's subscription to magazines and owning a credit card.

        what is actually disturbing, here in reality, is the privacy policies that each company declares on their site. Yes, Orkut has an INSANE privacy policy, but Tribe, Friendster and some smaller others look like they mean to make priva
  • by The Wing Lover ( 106357 ) <> on Monday February 16, 2004 @02:56PM (#8296273) Homepage
    Are we sure this is Slashdot?

    Oh, there it is, " food webs".
  • by barryfandango ( 627554 ) on Monday February 16, 2004 @02:56PM (#8296279)

    Check out the "highschool friendships []" diagram.

    I think I was the yellow dot on the far left.

  • by S3D ( 745318 ) on Monday February 16, 2004 @02:56PM (#8296280)
    There are a lot of work going in this direction now. For example here is an article about mapping weblog communities. Abstract: "Websites of a particular class form increasingly complex networks, and new tools are needed to map and understand them. A way of visualizing this complex network is by mapping it. A map highlights which members of the community have similar interests, and reveals the underlying social network. In this paper, we will map a network of websites using Kohonen's self-organizing map (SOM), a neural-net like method generally used for clustering and visualization of complex data sets. The set of websites considered has been the Blogalia weblog hosting site (based at this http URL), a thriving community of around 200 members, created in January 2002. In this paper we show how SOM discovers interesting community features, its relation with other community-discovering algorithms, and the way it highlights the set of communities formed over the network"
  • Conceptual Clumps (Score:5, Interesting)

    by _ph1ux_ ( 216706 ) on Monday February 16, 2004 @03:01PM (#8296318)
    I have been thinking about concept clumps - kind of similar to social clumps and cluster - but relating things that are based around similar ideas of information that they are trying to convey.

    Similar in the way that grokker clumps navigable areas together, it would be interesting to instead clump things together based on the relations of the meaning of the information they contain.

    For example, lets say that you are reading an article on any given site. You would be able to highlight a phrase, a word or a sentance, then look that term up in context. This is different than simply googling the term in that you are looking for the context of the term as opposed to a concrete definition.

    so if you were reading an article regarding the legal take over of a company by intel, you would be able to easily search for articles writen that involve intel in any other litigation, with results containing intel involved in purchases or sales of companies and their technologies coming to the top of the list...

    obviously there is a lot more in this required to accomplish it - so Ill just stop here before giving it all away.

    The main point being that this type of searching is easily applicable to understanding relationships in social networks as far as identifying how common intrests are shared.

    The clustering of attractions and dislikes to profile trends and personalities in any given demographic are made especially easy in systems such as friendster and orkut. By having people OPT-IN to the deepest marketing database available and provide you with all the details of not only the things they like (under the guise of sharing yourself with the others in the community) AND showing you what other people they are connected with who share common interests is one of the biggest social hijacks ever.

    Just when you thought marketing was a dead science that is too transparent to have any real impact, social networks arise to provide marketing data on an astounding level.

    [don tin foil hat]

    Just wait till they are able to correlate all this info with DNA profiles :)

    Not that this is bad per se, but it is a fact taht this info will be the next gold standard in market research where marketing will move to a social promotion system.

    I think that the goal here is the promotion of product will largely come from people advertising their likes of a product through their profiles and communications with friends online.

    It will be very easy for a group of people to communicate things (it already is) that are of interest to their social networks. Like on person telling the other 65,000 friends they have how they jsut experienced product Y, and that everyone should try it....

    interstingly, will we see fakesters made specifically to spam the other friends with testimonial like adverts for products they are trying to introduce to a specific demographic?
  • Link to paper (Score:4, Informative)

    by mrogers ( 85392 ) on Monday February 16, 2004 @03:05PM (#8296353)
    For those who are interested, a PDF of the paper mentioned in the article is here []. Running time is O(n^2) versus O(n^3) for previous algorithms, so don't go applying it to the Google cache just yet.
  • Where's the beef? (Score:4, Interesting)

    by Effugas ( 2378 ) on Monday February 16, 2004 @03:07PM (#8296365) Homepage
    I'm sure there's something really cool these guys are doing, but there is a very strong distinction between Big F*cking Huge Graphs (like we see a bunch of in the links) and Big F*cking Graph Analysis using some new technique, which isn't really clearly anywhere in there.

    I've been singing the praises of LGL [] as of late, pushing it into the Opte project (mass internet viz) and such, but truly the interesting applications involve analysis -- and where's the beef on that in this story?

  • by Guppy06 ( 410832 ) on Monday February 16, 2004 @03:07PM (#8296367)
    And his name is Kevin Bacon.
  • by Sideshow Coward ( 732864 ) on Monday February 16, 2004 @03:11PM (#8296410)
    Unfortunately, then only pattern in my social network is the singleton pattern.
  • ... and the brain (Score:5, Interesting)

    by Mazzaroth ( 519229 ) on Monday February 16, 2004 @03:13PM (#8296427) Homepage
    Social or internet networks are a lot like the brain
    • the wiring of it (topology) can gives a lot of insight on how it works and can even explain some emerging side effects.
    • it evolves with time - new connexions are made between nodes everyday, and we observe self-optimization.
    • the information that is communicated within the network itself is also pretty important. Actually, this is not only the tracer from which we derive its topology and its evolution, but also the very meaning of it.
    There is something way too similar about social networks, internet and the brain that really troubles me.
    • If you want to go further, read "Linked" by Albert-Laszlo Barabasi [] - they are similar, and their structure (basically nodes aren't randomly connected, but form "islands" and "supernodes", with weak-links between them. Some are isolated, but many link to each other. Due to isolation, however, it isn't possible to completely map by spidering the tree, regardless of the network. Thus, you end up with stuff like the "Invisible Web" - portions of the internet completely isolated from the main cluster. Social net
  • I'd like to check out the Orkut network--but have no friends (on it!). Would anyone be my friend? Just invite :)

  • by Anonymous Coward
    I go to the University of Michigan, and they even have classes where students examine social networks. It's interesting, but it's just used as an excuse to write a paper. It's engineering 100, a/k/a english for engineers.
  • by HarveyBirdman ( 627248 ) on Monday February 16, 2004 @03:18PM (#8296497) Journal
    I'm that little disconnected node way over in the dark corner. :-(
  • by Anonymous Coward
    you can see your social network online at Huminity []. They use a simple Google like interface (Google of people?) and show nodes and links maps of social networks. i think its the only "open" social network since in others u need to register before you can take a peek.
  • by Anonymous Coward
    A very interesting link in this regard can be found on []
  • Six degrees (Score:4, Interesting)

    by digitalhermit ( 113459 ) on Monday February 16, 2004 @04:07PM (#8297063) Homepage
    I'm in the middle of reading _Nexus_ by Mark Buchanan. One of the topics he covers is the work by Mark Granovetter that discusses links in a social network. One thing I found interesting was that weak links, those from friends-of-friends or casual associates, do more to tie together a network than the local, strong links. The reasoning is that local links tend to be more isolated: your friends will have similar interests and know many of the same people. Links to distant nodes will thus tend to be more "ordered" and require more steps to reach that node. Weak links will act as a shortcut between disparate groups.

  • by chia_monkey ( 593501 ) on Monday February 16, 2004 @04:24PM (#8297266) Journal
    The interesting thing to note about these social networks (which seems to have been overlooked) is that everyone will put different weights on what is important when deciding their social cirlcles. You can have ten people, with each having all the same interests. Soccer, computers, ramen noodles, Coors Light, Chihuahuas, and small-waisted women with big breasts. Yet each of these people will probably rank each differently. While one may go right up to Chihuahua lover at a party and strike up a conversation, another will go straight to the kitchen and see who else is looking at the ramen noodle collection.

    Basically, we have to find a way to "train" the software. It's not going to be easy. Training the TiVo still doesn't give you the best results. The personality compatibility tests sure are interesting, eh? Who here has been matched with the perfect roommate in college? Yet I haven't seen much yet on the weights of interests, just discussions about clusters of tight-knit social groups.
    INSNA is the professional association for researchers interested in social network analysis.
    CASOS brings together computer science, dynamic network analysis and the empirical study of complex socio-technical systems. Computational and social network techniques are combined to develop a better understanding of the fundamental principles of organizing, coordinating, managing and destabilizing systems of intelligent adaptive agents (human and artifici
  • by cr0sh ( 43134 ) on Monday February 16, 2004 @06:21PM (#8298446) Homepage well as any other "self-organizing" networks (such as the internet, and the brain) - you would do yourself good to read Albert-Laszlo Barabasi's [] book "Linked". This book will answer a whole lot of your questions (and in turn, it will inspire a whole slew more).

    Furthermore, read a few books on emergence (like Kevin Kelly's "Out of Control" []). Might as well also pick up and read Wolfram's "A New Kind of Science" []...

    I have said it before and I will say it again: Taken together, the knowledge within these three books could very well lead to some amazing breakthroughs in many of the sciences, in particular cognitive sciences and genetics. Even if some of the theories prove to be wrong, I think there is enough there to be a springboard for someone else - please read and decide for yourself!

  • The unspoken "Why?" of all of this is that companies are trying to understand our social connectedness as a way to SELL us things.

    Why use the scattershot TV ad to get us to buy a new car when they can simply allow the desirability of ownership trickle down the social food chain?

    This is "Keeping up with the Joneses" taken to perfection. Once it is calculated which other individual or group we all choose to imitate, you find that there are only 30 people in the world who have to be given that new promotio
  • by argStyopa ( 232550 ) on Monday February 16, 2004 @06:35PM (#8298577) Journal
    A researcher at the University of Michigan... ... a bright, promising freshman by the name of Hari Seldon....
  • These people rediscovered clustering methods from the 1950's.
  • by reiggin ( 646111 ) on Monday February 16, 2004 @08:26PM (#8299695)
    ... to develop a psychohistory model!

1 Angstrom: measure of computer anxiety = 1000 nail-bytes