Archive for category Networks

Beyond content searching: Google’s Knowledge Graph

Google started to roll out the Knowledge Graph, intended to be more about things rather than just strings. Delivering and disambiguating related content based on semantic network associations sounds great, if this really is a step forward to move out of the filter-bubble remains to be seen. Overall, it seems to be related to the idea of a conceptual graph, and wikipedia forms a big chunk of the underlying knowledge-base.

Further details:
techcrunch.com “Google Just Got A Whole Lot Smarter, Launches Its Knowledge Graph
Googles official blog “Introducing the Knowledge Graph: things, not strings
lifehacker.com “Google Knowledge Graph Brings Smarter Semantic Results to Your Google Searches
webpronews.com “Knowledge Graph: Google Gets Tight With Wikipedia

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Weekend Music

This one is so crisp and up-to-date it’s not even available yet in the non-virtual world: Santigold‘s new album “Master of My Make-Believe” will be out 23rd April (UK) / May 1st (US).

Seems the beancounters can’t find a proper box for this one in their desperate attempts to classify it, whether it’s retro dub, Neo Wave, hip-hop, drum’n’base or alternative – whatever, I don’t care as long as it sounds great and soothes my ears. Nice baseline – Yep, pump up the volume and enjoy!


Just in time I found this review “Network Science Reveals The Cities That Lead The World’s Music Listening Habits” on technologyreview.com via Social Foraging (“Dynamics of Social Interaction”, curated by Ashish Umre).

The evidence that ideas and fashions spread through society like viruses or like wildfire is compelling. Numerous studies have examined the networks in which this spread takes place and with increasingly large data sets to work with, researchers have become increasingly confident in their network-centric view of the world. These tools are teasing apart the large scale behaviour of humanity in ever increasing resolution.

The original article “The Geographic Flow of Music” by Conrad Lee and Pádraig Cunningham is available on arXiv:1204.2677v1.

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Nobel Prize in Computing


Pearl‘s book on “Causality” has been on my shelf for a while now. I also read it, a few times, but never managed to get through it in one go, cover to cover. Consequently, I haven’t come to grips with all details, implications and equations yet. No reason to worry about my intellectual capabilities, it’s quite fundamental and takes time to sink in. Now Judea Pearl has been awarded the 2011 ACM Turing Award – Congratulations!

The annual Association for Computing Machinery (ACM) A.M. Turing Award, sometimes called the “Nobel Prize in Computing,” recognizes Pearl for his advances in probabilistic and causal reasoning. His work has enabled creation of thinking machines that can cope with uncertainty, making decisions even when answers aren’t black or white. […]
The UCLA computer science professor is widely credited with coining the term “Bayesian Network,” which refers to a statistical model ACM describes as mimicking “the neural activities of the human brain, constantly exchanging messages without benefit of a supervisor.” Bayesian networks have been used to, among other things, analyze biological data for studies of medicine and diseases.

Here is a chance to see him talk for yourself:

“I compute, therefore I understand” – More videos are here on theScienceNetwork.

found via networkworld.com: Judea Pearl, a big brain behind artificial intelligence, wins Turing Award. See also on the ACM NEWS “Judea Pearl Wins 2011 ACM Turing Award“.

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The Network that (likes to think it) runs the World


Scientists at the ETH Zurich analysed the international ownership network of multi-national companies. If you had a look at the intrinsic properties of real-world and biological networks, the 80-20 rule comes as no surprise: in biological networks, usually over 80% of the edges are covered by less than 20% of the nodes. A related phenomenon is called the Pareto Principle in economics. The core of this network contained 1318 companies, which

… represented 20 per cent of global operating revenues, the 1318 appeared to collectively own through their shares the majority of the world’s large blue chip and manufacturing firms – the “real” economy – representing a further 60 per cent of global revenues …

“Reality is so complex, we must move away from dogma, whether it’s conspiracy theories or free-market,” says James Glattfelder.

It took me some time to find the original paper on PLoS, which isn’t linked from this article on the NewScientist – probably because it wasn’t out yet at the time:

Reference: The Network of Global Corporate Control by Stefania Vitali, James B. Glattfelder, Stefano Battiston (2011) PLoS ONE 6(10): e25995. doi:10.1371/journal.pone.0025995

How these findings relate to the error and attack tolerance of scale-free networks in the context of the current economic situation is further food for thought. But I urge caution to naïvely transfer insights from one domain to another, there are no simple (mono-causal) answers to complex problems. Especially when dealing with the emergent properties of networks, there is only one constant: they tend to work out quite differently from what we initially thought.

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Visualizing your Social Network

LinkedIn offers a visualisation of your network connections. While browsing and looking at the persons represented by the nodes and their proximity, directly hypothesis form in the head as to the common context (when and where one met). To me the accuracy of the layout and coloring is amazing! That the different clusters actually delineate different institutes and departments I had the pleasure to work with/at is a nice “proof-of-concept”, albeit a bit terrifying as to how much the network knows about us … if you already are at LinkedIn give it a spin to see “the community” emerging around yourself. Makes you also wonder what the guys running the social networks actually can do with the entirety of network data we dump on them. Anyway, have fun exploring your local network!

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Molecular Interaction Exchange

The International Molecular Exchange Consortium IMEx is the latest effort of data-providers to integrate Protein-Interaction Data –

IMEx provides

  • A non-redundant set of protein-protein interaction data from a broad taxonomic range of organisms
  • the data in standards compliant download formats (MITAB or PSI-MI XML 2.5)
  • Expertly curated from direct submissions or peer-reviewed journals to a consistent high standard.
[ … aiming to … ]
  • Develop and work to a single set of curation rules when capturing data from both directly deposited interaction data or from publications in peer-reviewed journals
  • Make these interaction available in a single search interface on a common website
  • Make all IMEx records freely accessible under the Creative Commons Attribution License

If you’ve been looking for that one-stop shop for getting a representative dataset of Protein-Protein Interactions, this just looks like it. There is an overview available on youtube (see below)

… and a training course on “Networks and Pathways Bioinformatics for Biologists” will take place at EMBL-EBI in May.

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Goodbye to hairballs?

You probably have seen the hairballs resulting from a force-directed layout of complex biological networks. What do they tell you? Well, that the networks are rather complex. But for much more detailed analysis the classical visualizations are actually quite useless. The hiveplot  is an attempt to provide

“A scalable, computationally fast, and straight-forward network visualization method that makes possible visual interpretation of network structure and evolution.”

A laudable goal, if it works in practice for you and your data – check it out. In addition there is an R package available for creating hive plots in 2D and 3D called HiveR.

Also see Krzywinski M, Birol I, Jones S, Marra M (2011). Hive Plots — Rational Approach to Visualizing Networks. Briefings in Bioinformatics (doi: 10.1093/bib/bbr069).

Thanks to Lucy Colwell for the hint!

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