Introductions to Graph Databases and Theory

As said before, I am getting deeper into graph-databases, specifically “neo4J “. The pace of development is breathtaking, it’s hard to keep up with the new versions and amazing features. In preparation of attending a “Cypher Hands On” (Meetup-Graph), I finally got round to updating to the latest 1.8M03 Milestone. By now, there are a couple of nice introductory videos available:

You might want to check out the videoGraphy @ neo4J. I also recommend the following Intro to Graph Databases (on vimeo) which has a nice explanation on what the buzz/whole point is all about plus some real world examples and history:

To deepen our understanding of the graph-theoretic foundations, I came across these books via blog.postmaster.gr:

“Graph Theory and Complex Networks: An Introduction” by Maarten van Steen. It is very interesting to note that this book is also available electronically as a personalised PDF. As the author notes: “When you write a book containing mathematical symbols, thinking big and acting commercially doesn’t seem the right combination. I merely hope to see the material to be used by many students and instructors everywhere and to receive a lot of constructive feedback that will lead to improvements. Acting commercially has never been one of my strong points anyway”.

– Reinhard Diestel: “Graph Theory“.

Working with #neo4j and #cypher I must take care not to: 1. get all addicted; 2. use it as a golden hammer for everything since it’s fun! @pavlobaron

It is fun, indeed. Enjoy!

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“

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.

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“.

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.

Ars Electronica

(not to be confused with ars technica) … Next time I make it to Austria, I’ll have to visit this exhibition (in Linz), maybe even the festival. Awesome, definitely.

New cultural techniques are emerging in the ever more tightly-knit global networks of digital technologies.

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!