You are currently browsing the tag archive for the ‘Social Networks’ tag.
Fig. – (book cover) “Emergence: The Connected Lives of Ants, Brains, Cities, and Software” (additional link) is a book written by media theorist Steven Berlin Johnson, published in 2001, Scribner. New York, NY.
“The patterns are simple, but followed together, they make for a whole that is wiser than the sum of its parts. Go for a walk; cultivate hunches; write everything down, but keep your folders messy; embrace serendipity; make generative mistakes; take on multiple hobbies; frequent coffee houses and other liquid networks; follow the links; let others build on your ideas; borrow, recycle; reinvent. Build a tangled bank.” — Steven Johnson.
Recent research have increasingly being focused on the relationship between Human-Human interaction, social networks (no, not the Facebook) and other Human-activity areas, like health. Nicholas Christakis (Harvard Univ. research link) points us that, people are inter-connected, and so as well, their health is inter-connected. This research engages two types of phenomena: the social, mathematical, and biological rules governing how social networks form (“Connection“) and the biological and social implications of how they operate to influence thoughts, feelings, and behaviours (“Contagion“), as in the self-organized stigmergy-like dynamics of Cognitive Collective Perception (link).
Above, Nicholas Christakis (in a 56m. documentary lecture produced by The Floating University, Sept. 2011) discusses the obvious tension and delicate balance between agency (one individual choices and actions) and structure (our collective responsibility), where here, structure refers not only to our co-evolving dynamic societal environment as well as to the permanent unfolding entangled nature of topological structure on complex networks, such as in human-human social networks, while asking: If you’re so free, why do you follow others? The documentary (YouTube link) resume states:
“If you think you’re in complete control of your destiny or even your own actions, you’re wrong. Every choice you make, every behaviour you exhibit, and even every desire you have finds its roots in the social universe. Nicholas Christakis explains why individual actions are inextricably linked to sociological pressures; whether you’re absorbing altruism performed by someone you’ll never meet or deciding to jump off the Golden Gate Bridge, collective phenomena affect every aspect of your life. By the end of the lecture Christakis has revealed a startling new way to understand the world that ranks sociology as one of the most vitally important social sciences.”
While cooperation is central to the success of human societies and is widespread, cooperation in itself, however, poses a challenge in both the social and biological sciences: How can this high level of cooperation be maintained in the face of possible exploitation? One answer involves networked interactions and population structure.
As perceived, the balance between homophily (where “birds of a feather flock together”) and heterophily (one where most of genotypes are negatively correlated), do requires further research. In fact, in humans, one of the most replicated findings in the social sciences is that people tend to associate with other people that they resemble, a process precisely known as homophily. As Christakis points out, although phenotypic resemblance between friends might partly reflect the operation of social influence, our genotypes are not materially susceptible to change. Therefore, genotypic resemblance could result only from a process of selection. Such genotypic selection might in turn take several forms. For short, let me stress you two examples. What follows are two papers, as well as a quick reference (image below) to a recent general-audience of his books:
1) Rewiring your network fosters cooperation:
“Human populations are both highly cooperative and highly organized. Human interactions are not random but rather are structured in social networks. Importantly, ties in these networks often are dynamic, changing in response to the behavior of one’s social partners. This dynamic structure permits an important form of conditional action that has been explored theoretically but has received little empirical attention: People can respond to the cooperation and defection of those around them by making or breaking network links. Here, we present experimental evidence of the power of using strategic link formation and dissolution, and the network modification it entails, to stabilize cooperation in sizable groups. Our experiments explore large-scale cooperation, where subjects’ cooperative actions are equally beneficial to all those with whom they interact. Consistent with previous research, we find that cooperation decays over time when social networks are shuffled randomly every round or are fixed across all rounds. We also find that, when networks are dynamic but are updated only infrequently, cooperation again fails. However, when subjects can update their network connections frequently, we see a qualitatively different outcome: Cooperation is maintained at a high level through network rewiring. Subjects preferentially break links with defectors and form new links with cooperators, creating an incentive to cooperate and leading to substantial changes in network structure. Our experiments confirm the predictions of a set of evolutionary game theoretic models and demonstrate the important role that dynamic social networks can play in supporting large-scale human cooperation.”, abstract in D.G. Rand, S. Arbesman, and N.A. Christakis, “Dynamic Social Networks Promote Cooperation in Experiments with Humans,” PNAS: Proceedings of the National Academy of Sciences (October 2011). [full PDF];
Picture – (book cover) Along with James Fowler, Christakis has authored also a general-audience book on social networks: Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives, 2011 (book link). For a recent book review, access here.
2) We are surrounded by a sea of our friends’ genes:
“It is well known that humans tend to associate with other humans who have similar characteristics, but it is unclear whether this tendency has consequences for the distribution of genotypes in a population. Although geneticists have shown that populations tend to stratify genetically, this process results from geographic sorting or assortative mating, and it is unknown whether genotypes may be correlated as a consequence of nonreproductive associations or other processes. Here, we study six available genotypes from the National Longitudinal Study of Adolescent Health to test for genetic similarity between friends. Maps of the friendship networks show clustering of genotypes and, after we apply strict controls for population strati!cation, the results show that one genotype is positively correlated (homophily) and one genotype is negatively correlated (heterophily). A replication study in an independent sample from the Framingham Heart Study veri!es that DRD2 exhibits signi!cant homophily and that CYP2A6 exhibits signi!cant heterophily. These unique results show that homophily and heterophily obtain on a genetic (indeed, an allelic) level, which has implications for the study of population genetics and social behavior. In particular, the results suggest that association tests should include friends’ genes and that theories of evolution should take into account the fact that humans might, in some sense, be metagenomic with respect to the humans around them.”, abstract in J.H. Fowler, J.E. Settle, and N.A. Christakis, “Correlated Genotypes in Friendship Networks,” PNAS: Proceedings of the National Academy of Sciences (January 2011). [full PDF].
Photo – The Aftermath Network research group: Manuel Castells, Terhi Rantanen, Michel Wieviorka, Sarah Banet-Weiser, Rosalind Williams, John Thompson, Gustavo Cardoso, Pekka Himanen, You-Tien Hsing, Ernesto Ottone, João Caraça and Craig Calhoun.
Oh!… nostalgia. But can you read between the lines? Could you perceive the cynical TV ads. The underlying media mantra that you are not being productive enough. That is you, ultimately the reason for the global crisis. That ‘something‘ went broken. Are you having a feeling that all this mess could give rise to National Socialism, again? That, reversed nostalgia plays a role too?! Well, … shortly after the beginning of the financial crisis of 2008 sociologist Manuel Castells gathered a small group of international top intellectuals to ponder the crisis. While the crisis expanded, Castells named his group ‘The Aftermath Network‘, a direct reference to the new world which according to him will emerge from the ashes of the crisis.
Under the venue and patronage of Calouste Gulbenkian Foundation, Lisbon-Portugal, Castell‘s multidisciplinary research group meet every year with the aim of discussing in real time and from different angles the societal and cultural consequences of the worldwide economic collapse. Now, thanks to the Dutch VPRO Backlight, a new documentary has been produced (uploaded last week over YouTube), reflecting part of those meetings. Entitled ‘Aftermath of a Crisis‘ (above) is a 48 minute documentary reporting the world incertitude, facing a global fallacy, as well as the emergence of new social movements and protests in Spain, Greece, Portugal and London. Unfortunately, as I said the other day (link), there are increasing signs that: Keynesianism is now Bankism. Know what? Next time someone or some institution comes to you covered by a veil of nostalgia, even a thin one, do yourself a favor: put your brain in maximum alert.
Fig. – A cartoon by Nitrozac and Snaggy (The Joy of Tech, link) July 2011 [Geek Culture]. Click to enlarge.
Graphic – Internet traffic to and from Egypt on January 27 2011. At 5:20 pm EST, traffic to and from Egypt across 80 Internet providers around the world drops precipitously (source: Arbor Networks). [click to enlarge]
That vertical axis (MBps) is the number of MuBarak’s people support, right?!
“Conclusion: When you shut-down the Internet, people will click on real physical streets… #Egypt”, @ViRAms, Jan. 28. 2011.
“I was never interested in Facebook or MySpace because they feel like malls to me. Twitter actually feels like the street. You can bump into anybody on Twitter.” — Science-fiction novelist William Gibson – New York (October 11, 2010)
“There is almost certainly an evolutionary drive toward increasing complexity in the face of entropy. That’s practically a definition of life. Technology is so powerful and attractive to us because it holds the promise of greater complexity and greater connectedness. Atoms to molecules to cells to organelles to organisms. What’s next? No one knows for sure, but it sure ain’t Facebook.” — American media theorist Douglas Rushkoff, writer, columnist, early cyberpunk culture adopter, and advocacy of open source solutions to social problems.
Precursors of social networks in the late 1800s include Émile Durkheim and Ferdinand Tönnies. Tönnies argued that social groups can exist as personal and direct social ties that either link individuals who share values and belief (gemeinschaft) or impersonal, formal, and instrumental social links (gesellschaft) [in Linton Freeman, “The Development of Social Network Analysis“, Vancouver, Empirical Press, 2004 (here is a valuable must-read review on it)]. Durkheim gave a non-individualistic explanation of social facts arguing that social phenomena arise when interacting individuals constitute a reality that can no longer be accounted for in terms of the properties of individual actors. He distinguished between a traditional society – “mechanical solidarity” – which prevails if individual differences are minimized, and the modern society – “organic solidarity” – that develops out of cooperation between differentiated individuals with independent roles. Then, Georg Simmel (1908-1971), writing at the turn of the twentieth century, was the first scholar to think directly in social network terms. His essays pointed to the nature of network size on interaction and to the likelihood of interaction in ramified, loosely-knit networks rather than groups.
Nowadays, however, the paraphernalia of increasing intelligent tools (network metrics) are widely available, mainly to the exponential role of the science of complex networks. As stated by Matthias Scholz (Network Science.org / Webpage) (…) Network science has received a major boost caused by the widespread availability of huge network data resources in the last years. One of the most surprising findings, popularized by Albert-László Barabási and his team, is that real networks behave very distinct from traditional assumptions of network theory. Traditionally, real networks were supposed to have a majority of nodes of about the same number of connections around an average. This is typically modelled by random graphs. However, modern network research revealed that the majority of nodes of real networks is very low connected, and, by contrast, there exists some nodes of very extreme connectivity (hubs). This power-law characteristics, termed scale-free by Barabási, can be found in many complex real networks from biological (natural) to social man-made networks (…).
Book cover – Linton Freeman, “The Development of Social Network Analysis“, Vancouver, Empirical Press, 2004 (and a valuable review on it).
While embedding themselves on social-networking, people do tend to forget this, of course, but here are 2 or 3 things you should know about Social Networks before stupefying registering yourself on FarmVille (actually, this is a limited list of some of the actual metrics usually employed on current network analysis provided with a short description – So, … do really ponder yourself where you are – on the street or in the mall!):
Betweenness (link) The extent to which a node lies between other nodes in the network. This measure takes into account the connectivity of the node’s neighbours, giving a higher value for nodes which bridge clusters. The measure reflects the number of people who a person is connecting indirectly through their direct links. | Bridge (link) An edge is said to be a bridge if deleting it would cause its endpoints to lie in different components of a graph. | Centrality (link) This measure gives a rough indication of the social power of a node based on how well they “connect” the network. “Betweenness”, “Closeness”, and “Degree” are all measures of centrality. | Centralization (link) The difference between the number of links for each node divided by maximum possible sum of differences. A centralized network will have many of its links dispersed around one or a few nodes, while a decentralized network is one in which there is little variation between the number of links each node possesses. | Closeness (link) The degree an individual is near all other individuals in a network (directly or indirectly). It reflects the ability to access information through the “grapevine” of network members. Thus, closeness is the inverse of the sum of the shortest distances between each individual and every other person in the network. The shortest path may also be known as the “geodesic distance”. | Clustering coefficient (link) A measure of the likelihood that two associates of a node are associates themselves. A higher clustering coefficient indicates a greater ‘cliquishness’. | Cohesion (link) The degree to which actors are connected directly to each other by cohesive bonds. Groups are identified as ‘cliques’ if every individual is directly tied to every other individual, ‘social circles’ if there is less stringency of direct contact, which is imprecise, or as structurally cohesive blocks if precision is wanted. | …
Fig. – Hue (from red=0 to blue=max) shows the node betweenness. (link)
… Degree (link) The count of the number of ties to other actors in the network. See also degree (graph theory). | (Individual-level) Density (link) The degree a respondent’s ties know one another/ proportion of ties among an individual’s nominees. Network or global-level density is the proportion of ties in a network relative to the total number possible (sparse versus dense networks). | Flow betweenness centrality (link) The degree that a node contributes to sum of maximum flow between all pairs of nodes (not that node). | Eigenvector centrality (link) A measure of the importance of a node in a network. It assigns relative scores to all nodes in the network based on the principle that connections to nodes having a high score contribute more to the score of the node in question. | Local Bridge (link) An edge is a local bridge if its endpoints share no common neighbors. Unlike a bridge, a local bridge is contained in a cycle. | Path Length (link) The distances between pairs of nodes in the network. Average path-length is the average of these distances between all pairs of nodes. | Prestige (link) In a directed graph prestige is the term used to describe a node’s centrality. “Degree Prestige”, “Proximity Prestige”, and “Status Prestige” are all measures of Prestige. See also degree (graph theory). | Radiality (link) Degree an individual’s network reaches out into the network and provides novel information and influence. | Reach (link) The degree any member of a network can reach other members of the network. | Structural cohesion (link) The minimum number of members who, if removed from a group, would disconnect the group. | Structural equivalence (link) Refers to the extent to which nodes have a common set of linkages to other nodes in the system. The nodes don’t need to have any ties to each other to be structurally equivalent. | Structural hole (link) Static holes that can be strategically filled by connecting one or more links to link together other points. Linked to ideas of social capital: if you link to two people who are not linked you can control their communication.
Of course, some of these metrics are redundant over each other, and in fact there is some intelligence on having this on-purpose redundancy. But I would say that Path Length and Cliqueness, Eigenvector centrality, Betweenness, Clustering coefficient, Degree and last but not least Flow (btw, here‘s my own poetic lateral view of it), would be the most important of them all, even if, these all depends on what you are, on what you see, on what you feel, and mostly on what you somehow rather expect from it as a whole experience over time. So, finally, let me just add that all these metrics will not avoid people from writing/sharing experiences like: “Son las 7 y 30 y estoy cagando” followed by “Son las 5 y 31 y ya cagado“. Unfortunately, what follows is precisely a video parody on Facebook not far from his own reality. As usual, you are always free to choose from the geriatric-like shelter malls or the open-air streets…
Fig. – Complex Networks (examples): (a) the Internet, where nodes are routers and edges show physical network connections. (b) an ecosystem (c) professional collaboration networks between doctors; and (d) rail network of Barcelona, where nodes are subway stations and edges represent rail connections. (ANU E Press: Australian University Press)
[…] Social networks are either a) going to morph into storytelling media that provide tools to construct narrative on top of the update stream, or b) are going to stop growing as people seek out a different set of tools that are better for communication and storytelling than social networks, which do a mediocre job at both. Part of where I think social networks need to move is to give people the ability to author stories, and to recruit and notify others that they are part of that story. The best online games make this explicit – you participate precisely because you want to be part of a story. Joining a group isn’t a story. Stories have focal points – beginnings and ends. […], Anthony Townsend, “The Future of Social Networks is Storytelling” (part 2), Feb. 2010 [link].
[…] What about writing should be cherished? Calvino, in a wonderfully simple scheme, devotes one lecture (a memo for his reader) to each of five indispensable literary values. First there is “lightness” (leggerezza), and Calvino cites Lucretius, Ovid, Boccaccio, Cavalcanti, Leopardi, and Kundera–among others, as always–to show what he means: the gravity of existence has to be borne lightly if it is to be borne at all. There must be “quickness,” a deftness in combining action (Mercury) with contemplation (Saturn). Next is “exactitude,” precision and clarity of language. The fourth lecture is on “visibility,” the visual imagination as an instrument for knowing the world and oneself. Then there is a tour de force on “multiplicity,” where Calvino brilliantly describes the eccentrics of literature (Elaubert, Gadda, Musil, Perec, himself) and their attempt to convey the painful but exhilarating infinitude of possibilities open to humankind.
The sixth and final lecture – worked out but unwritten – was to be called “Consistency.” Perhaps surprised at first, we are left to ponder how Calvino would have made that statement, and, as always with him, the pondering leads to more. With this book Calvino gives us the most eloquent, least defensive “defense of literature” scripted in our century – a fitting gift for the next millennium. […], Harvard University Press on the Charles Eliot Norton Lectures, (book) Italo Calvino “Six Memos for the next Millenium“. [link]
Figure – Book cover of Toby Segaran’s, “Programming Collective Intelligence – Building Smart Web 2.0 Applications“, O’Reilly Media, 368 pp., August 2007.
{scopus online description} Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting data-sets from other web sites, collect data from users of your own applications, and analyze and understand the data once you’ve found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general — all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application.
{even if I don’t totally agree, here’s a “over-rated” description – specially on the scientific side, by someone “dwa” – link above} Programming Collective Intelligence is a new book from O’Reilly, which was written by Toby Segaran. The author graduated from MIT and is currently working at Metaweb Technologies. He develops ways to put large public data-sets into Freebase, a free online semantic database. You can find more information about him on his blog: http://blog.kiwitobes.com/. Web 2.0 cannot exist without Collective Intelligence. The “giants” use it everywhere, YouTube recommends similar movies, Last.fm knows what would you like to listen and Flickr which photos are your favorites etc. This technology empowers intelligent search, clustering, building price models and ranking on the web. I cannot imagine modern service without data analysis. That is the reason why it is worth to start read about it. There are many titles about collective intelligence but recently I have read two, this one and “Collective Intelligence in Action“. Both are very pragmatic, but the O’Reilly’s one is more focused on the merit of the CI. The code listings are much shorter (but examples are written in Python, so that was easy). In general these books comparison is like Java vs. Python. If you would like to build recommendation engine “in Action”/Java way, you would have to read whole book, attach extra jar-s and design dozens of classes. The rapid Python way requires reading only 15 pages and voila, you have got the first recommendations. It is awesome!
So how about rest of the book, there are still 319 pages! Further chapters say about: discovering groups, searching, ranking, optimization, document filtering, decision trees, price models or genetic algorithms. The book explains how to implement Simulated Annealing, k-Nearest Neighbors, Bayesian Classifier and many more. Take a look at the table of contents (here: http://oreilly.com/catalog/9780596529321/preview.html), it does not list all the algorithms but you can find more information there. Each chapter has about 20-30 pages. You do not have to read them all, you can choose the most important and still know what is going on. Every chapter contains minimum amount of theoretical introduction, for total beginners it might be not enough. I recommend this book for students who had statistics course (not only IT or computing science), this book will show you how to use your knowledge in practice _ there are many inspiring examples. For those who do not know Python – do not be afraid _ at the beginning you will find short introduction to language syntax. All listings are very short and well described by the author _ sometimes line by line. The book also contains necessary information about basic standard libraries responsible for xml processing or web pages downloading. If you would like to start learn about collective intelligence I would strongly recommend reading “Programming Collective Intelligence” first, then “Collective Intelligence in Action”. The first one shows how easy it is to implement basic algorithms, the second one would show you how to use existing open source projects related to machine learning.
Chemoton § Vitorino Ramos' research notebook 
Recent Comments