The “core” that Steve Jobs is talking about can be seen as a tightly-knit network of several people where most of the nodes have the same number of connections to each other. They then decide who else makes it into the larger network of the company’s employees. It’s also interesting how most people say that they’ve been given a chance that no one else would have ever given them. This slight misplacement of the “professional” skills and the actual job that the person had to do seemed to produce a gap that was motivating enough for people to approach their tasks in a more innovative and fervent way.

You can watch Ross Anderson’s presentation on AssystComplexity’s website (unfortunately, they don’t allow their videos to be embedded elsewhere).

We find this research very interesting in relation to the studies of how human brain neural network structure evolves with age (see Sporns 2010 for an overview). It has been shown that young adults tend to have clearly defined distinct communities of neurons that are well connected globally. With age, these communities change in structure, separating into the smaller ones and generally the overall connectivity between them increases. According to this research on memory ageing, the cognitive tasks which aim to maintain segregation between various neuronal clusters within the brain could be potentially effective in preventing memory loss. We are interested, however, what other capacities emerge when the structure of neural network changes with age. Maybe the loss of memory is accompanied with an increase capacity in other cognitive tasks, which have not been given sufficient relevance in previous research. We would like to see more studies that use different criteria for evaluating cognitive performance.

Emergence of hierarchical networks and polysynchronous behaviour in simple adaptive systems (PDF)

We describe the dynamics of a simple adaptive network. The network architecture evolves to a number of disconnected components on which the dynamics is characterized by the possibility of differently synchronized nodes within the same network (polysynchronous states). These systems may have implications for the evolutionary emergence of polysynchrony and hierarchical networks in physical or biological systems modeled by adaptive networks.

Driven by various kinds of noise, ensembles of limit cycle oscillators can synchronize. In this letter, we propose a general formulation of synchronization of the oscillator ensembles driven by common colored noise with an arbitrary power spectrum. To explore statistical properties of such colored noise-induced synchronization, we derive the stationary distribution of the phase difference between two oscillators in the ensemble. This analytical result theoretically predicts various synchronized and clustered states induced by colored noise and also clarifies that these phenomena have a different synchronization mechanism from the case of white noise.

It has also been shown that one of the more efficient immunization strategies is to focus on the hubs (the most connected nodes), as well as introducing a certain degree of disassortativity in the network’s topology (encouraging periphery members to connect to each other). The abstract of the paper is below:

By analysing the diffusive dynamics of epidemics and of distress in complex networks, we study the effect of the assortativity on the robustness of the networks. We first determine by spectral analysis the thresholds above which epidemics/failures can spread; we then calculate the slowest diffusional times. Our results shows that disassortative networks exhibit a higher epidemiological threshold and are therefore easier to immunize, while in assortative networks there is a longer time for intervention before epidemic/failure spreads. Moreover, we study by computer simulations the sandpile cascade model, a diffusive model of distress propagation (financial contagion). We show that, while assortative networks are more prone to the propagation of epidemic/failures, degree-targeted immunization policies increases their resilience to systemic risk.

Client: PLAYBerlin platform
Objective: Find the key people in several local music and art groups in order to know more about what’s happening in their respective scenes.

For this job we were contacted by PLAYBerlin online music and arts platform to make analysis of three different music and art groups based in Berlin. Our objective was to find the most active people and to invite them to join PLAYBerlin network, so they could be informed about each others’ activity and post their news and events on PLAYBerlin.Com website. Knowing the most connected people in each group would also help PLAYBerlin to know who are the people generating most of the activity in the local scene.

The first step is to know what the actual communities should be. In this case, it was Vulkaandance – the group that organises monthly parties around the city, Ninety-Five – a small gallery-club, and 032c (the largest group) – the group associated with 032c art and culture bi-annual magazine published in Berlin. The interest here was to see how the local alternative music scene and the alternative art scene connect. What are the people that are interested in interactions between the groups in particular?

Approaching these groups is difficult, because there are more than 400 members in each, so it’s not easy to know who to contact first. Instead, we decided to identify the people who have the most connections both within their own group and also to the other groups analysed. This way we would find the individuals who are involved across the different communities and would most likely to be involved into PLAYBerlin project, which is an online platform connecting music, performance and contemporary dance.

We exported the groups data using NetVizz application and opened the graphs using Gephi visualisation software. The nodes in the graph designate the Facebook profiles and the connections are the friend links between them. A more precise analysis would also include some sort of measurement of communication intensity between the people in the network (e.g. the number of “likes” they recently left to each other’s content), however, in this case we were more interested in the structural properties of the network, rather than the functional ones.

In order to render the graph more readable, we applied ForcedAtlas layout, ranged the nodes according to their betweenness centrality (the higher the value,  the more is the node’s relative influence in the network), and ran a community detection algorithm that shows the groups of nodes that are more densely connected to each other than to the rest of the network. We obtained this image as a result:

We then left only 5% (133 out of 2660) of the most connected nodes in order to have a clearer picture of influence in this network. This produced a more readable image of the most influential profiles for these three groups combined.

As we can see, there are four distinct communities in the three groups designated by different colors. The biggest one is the blue (most influential nodes are Arcademi – an online platform for art and design, Joerg Koch – the editor of 032c magazine), and Mono Kultur (another small art and fashion magazine from Berlin). The green one is smaller and the most influential nodes are Alexis Zavialoff (works for a popular art book distribution company Motto Berlin) and Sang Bleu (also publishing). The yellow one is mainly the music group from Vulcandance where the most influential nodes are Nomad Uno (a DJ) and Emil Angelov (another DJ). The pink group (Carson Chan – writer and journalist, Maxime Ballesteros – photographer) is very interesting, because they have connections across the different groups and play quite an important roles in bringing the communities together. Also Gianni Calvini is an important node in the network, as he connects the music and art communities together.

What can we do with this information?

1) We identified the 10 individuals among the initial 2500 people and can now contact them directly using Facebook message facility or its advertising service.

2) We also got a much better insight into the main interests that unify these communities. As we’ve seen, the art group that we were researching gravitated towards the people running the art magazine / distribution channels. The music group gravitated towards the DJs. The people who link those groups together are mainly the journalists and photographers who are probably involved into activities of all these groups to some extent.

3) We can use this information  to make links between the groups, deliver the messages to these communities in a more focused way, as well as access the information that they distribute more efficiently.

The same approach can be used if you want to be quickly introduced to a new scene at a new city. Find a few groups you find interesting, identify the key members using some basic social network analysis, check out their profiles and contact them for more information. This is a fast and efficient way of scouring through very complex (and not easily available) information, that can help people connect bypassing the content filters imposed by Facebook. There is of course the question of privacy, but as we’re focusing on the most connected profiles here, it is likely that the individuals identified will be more or less public personas that welcome this sort of attention.

Client: Way to Russia travel guide (the most popular online guide to Russia in English, 1.5 Mln visitors annually).
Objective: Build the initial audience and a network of followers who would be most interested in reposting the content from Way to Russia’s new Tumblr blog.

Just like other social platforms Tumblr doesn’t give you enough tools to have an overview of their network. However, they have a great “Notes” feature, which lists all the reposts that the post have gotten. For example, if you find a popular image you can always see who posted it first and who reposted it afterwards. Our task is to build a social graph from this data. We can then trace how the piece of content “traveled” through the network. If we repeat the same operation for several posts and find some regular patterns, we would be able to identify the most influential posters (hubs) and communities (groups of people that tend to repost each other’s content).

Let’s use a real example. We have just set up a new Tumblr blog for our client, Way to Russia Guide – a popular internet resource about Russia. How do we quickly build the audience that is relevant for the content they are going to post? First, let’s look for the posts tagged “Russia” by opening this link: http://www.tumblr.com/tagged/russia
. Let’s open the ones that have the most “notes” or feedback on them. These are by users millionsmillions, fuckyeahabandonedplaces, iheartmoscow (a few posts can be analysed), passionaterussian, and touchrussia. Many more blogs could be added, but we’ll stop here to keep this example simple.

The next step is to copy and paste all the repost data into a text file. It will look something like this:

Each “A reblogged this from B” statement is a link between two profiles (A and B). That’s the information we need.  The next step is to remove the stuff we don’t need (Liked posts and comments) and replace ” reblogged this from ” with a comma “,” so that we have a nice and neat CSV file that just lists the links between the profiles. This can easily be done by search / replace function of any editor. In the end we get something like this:

The final step is to open this .csv file in Gephi and visualize the network.

Then we should apply forced atlas layout…

Surprisingly, even though we used different blogs found through “russia” tag, it’s a connected graph, meaning that the data we extract from this analysis will be quite relevant for us. If we “follow” the main hubs in this network and thus make them notice our content, we’ll have access to the whole network.

Then we can delete the “orphan” nodes (the ones that were just “liking” the content – there are about 50% of them in this graph. We also make the nodes with the higher betweenness centrality bigger (those profiles that appear more often on the shortest path between any two randomly chosen nodes in the network – a measure of the node’s influence in the network).

And finally turn on the captions to see what these profiles are…

The most influential ones in the network are iheartmoscow, fuckyeahabandonedplaces, khrushchev-is-my-homeboy (interesting, because we didn’t notice this one before, but this blog was often reblogged by others, although it did not originate many posts by itself – a sign of an important hub for proliferating content across the network), russophilia. Also, the two very important ones are integral87, tvoya-krasatina (both were not detected before, because they often link different hubs together, but they didn’t originate the content we found) and vogueofrussia, as they connect different communities together. Touchrussia and iloveeurope are also important hubs.

The final step is to “Follow” all these blogs on Tumblr and to re-blog or like some of their posts. This is usually enough to get noticed and followed back, making it much more likely that they will see and interact with your own content.

Once you accumulate quite a few Tumblr friends and reblog some of their posts, just look for the ones that have the most “notes” and repeat the procedure described above. This will allow you to build a strong and interconnected network. It’s also important to sometimes go outside of one’s immediate “filter bubble” and continue to search for the relevant “tagged” content that doesn’t belong to your immediate circle of Tumblr friends. This will enable you to always expand your reach and find the new and interesting information to share with your readers that is also unique to the blog circle that you belong to.

Let us know if you’d like us to do such analysis for your blog or if you have any questions.

One of the most interesting papers on this subject is called “Nonlinear Dynamics of the Brain: Emotion and Cognition” published in 2010 by Rabinovich & Muezzinoglu in Physics Uspekhi journal. The English version of this article is available to subscribers only, but the Russian version is available for free. We will provide a short summary of this paper below.

Rabinovich & Muezzinoglu propose to model a dynamical system where the change of emotional and cognitive functions over time is plotted into the phase space. The data can be gathered by detecting synchronization between various groups of neurons responsible for emotional and cognitive activity (at the according frequency range and cortical locations within the brain). The authors propose to model the interaction between the emotional and cognitive processes as winner-less competition (Just & Varma 2007), using Lottke-Volter equations applied to model prey-predator dynamics. This interaction plotted in the phase space produces strange attractors, which are characteristic for open dissipative systems where the density of all interacting flows (energy, material, information) is stable (see Figures 1 and 2).

Strange attractors within the brain produce local areas of metastability through synchronization

The strange attractors may be associated with a certain emotional state, cognitive activity, or memory (Rabinovich & Muezzinoglu 2010; Wills et al 2009). These local “wells” of non-equilibrium stability interact on a wider scale through the global workspace (a core network of long-range connections performing integrative function usually associated with consciousness). They synchronize at a higher gamma (30-80 Hz) frequency to produce global attractors. Small perturbations do not affect these stable states: robustness of the system (Kitano 2004) ensures that it comes back towards attractor even when disturbed. A certain level of noise ensures that the system is not “asleep” (Rabinovich et al 2008). A change in dynamics between emotion and cognition processes provokes activation of different local attractor states, which, in turn, provoke global shifts of the system. Therefore, the system is metastable in that it has several possible dynamic states but only one is actualized at every moment of time.

Metastability and global stability

Local shifts in emotion-cognition dynamics provoke global shifts of the system from one state to another along a stable heteroclinic channel, which traces the transition of the system across equilibrium points over time (Rabinovich et al 2008; Rabinovich & Muezzinoglu 2010; Bystritsky et al 2012). The idea that brain networks evolve along a trajectory of attractor points accompanied by a certain level of chaotic noise was also expressed by Tsuda (2001) in his discussion of “chaotic itinerancy” found in multi-dimensional dynamical systems. Temporary global stability of such system may be referred to as mood (an state emerging as a result of global integration between various strange attractors). A study (Katerndahl et al 2007) has shown that mood transitions in mentally healthy patients have a chaotic variability.

Chaotic itinerancy of cognitive and emotional states

References:

Bystritsky, a, Nierenberg, a a, Feusner, J. D., & Rabinovich, M. (2012). Computational non-linear dynamical psychiatry: A new methodological paradigm for diagnosis and course of illness. Journal of psychiatric research, 46(4), 428-435. Elsevier Ltd. doi:10.1016/j.jpsychires.2011.10.013

Katerndahl, D., Ferrer, R., Best, R., & Wang, C.-P. (2007). Dynamic patterns in mood among newly diagnosed patients with major depressive episode or panic disorder and normal controls. Primary care companion to the Journal of clinical psychiatry, 9(3), 183-7. Retrieved from http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1911176&tool=pmcentrez&rendertype=abstract

Rabinovich, M. I., & Muezzinoglu, M. K. (2010). Nonlinear dynamics of the brain: emotion and cognition. PhysicsUspekhi, 53(4), 357-372. doi:10.3367/UFNe.0180.201004b.0371

Rabinovich, M. I., Huerta, R., Varona, P., & Afraimovich, V. S. (2008). Transient cognitive dynamics, metastability, and decision making. PLoS computational biology, 4(5), e1000072. doi:10.1371/journal.pcbi.1000072

Tsuda, I. (2001). Toward an interpretation of dynamic neural activity in terms of chaotic dynamical systems. The Behavioral and brain sciences, 24(5), 793-810; discussion 810-48. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/12239890

Wills, T. J., Lever, C., Cacucci, F., Burgess, N., & Keefe, J. O. (2009). UKPMC Funders Group Attractor Dynamics in the Hippocampal Representation of the Local Environment, 308(5723), 873-876. doi:10.1126/science.1108905.

TED Talks
Probably the better-known platform that hosts talks on technology, education and design. The good thing about TED is that you can start your own focused discussion next to each lecture, making it a great platform to meet like-minded people around very specific subjects of inquiry.
http://www.ted.com

Khan Academy
A popular peer-to-peer learning portal. You can either be a student, put up your own courses, or help others to learn the stuff you already know. It caters more to college students, but may be a good place to refresh some of the basics.
http://www.khanacademy.org

Udemy
Another peer-to-peer educational portal. Anyone can put up a free or paid course online – most of them are presented as slide shows. Udemy is more practically oriented: you can learn about HTML5, programming, web design, marketing skills and stuff like that.
http://www.udemy.com/

VideoLectures
Hosts hundreds of lectures from the world’s best professors (such as Mark Newman, who advanced network science dramatically over the past 15 years). You can also search lectures by university (e.g. Yale) or by the subject.
http://videolectures.net/

Mendeley
An academic collaboration network. Helps you keep track of all the papers you’re researching, organized by the subject. You can also start groups of research and add other people as well as the relevant publications in them. Could do with a social layer, but so far the best of the kind nevertheless.
http://mendeley.com

iTunes
Surprisingly, iTunes has a huge selection of educational courses from the world’s best universities (both video and audio), but also – a huge range of educational podcasts. Just make a search for the topics or the author you’re interested in and probably you’ll find something you like.

Great Courses
Features complete courses on various subject from chaos to differential equations from the world’s best professors. It’s a bit of a one-way enterprise and the courses are expensive, but you can find some really good stuff on there.
http://www.thegreatcourses.com