Posts Tagged Graph

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!

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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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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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Watching them watching us

Collusion is a plug-in for FireFox that visualises the sites that track your movements on the web – and then displays the results for you as a directed graph. Each node represents one particular web-site, each edge a “tracking through” relationship. After installation, collusion summarizes the data on the trackers. After a bit of the usual browsing you might be in for a bit of a surprise as you can almost see your digital footprint grow in real-time. No worries, it just displays the data that is gathered by companies on you, so it helps to get a better idea what your rights to electronic self-determination might entail.

Privacy Policy: When you’re using the add-on, we collect sites you visit solely to show you how they’re connected. We don’t keep them and don’t give away the information to anyone except you.

It is quite educational to see what the central nodes are – google of course, as you might expect, is one of them. But ever heard of ScoreCardResearch?

See also the collusion blog for more background info and links to the (open source) code – additional references are lifehacker.com: “Collusion for Firefox Shows You Who’s Tracking You on the Web In Real Time” and (german) heise.de: “Add-On für Firefox visualisiert Webseiten-Tracking” (Permalink)

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Visualizing Biological Data

The VizBi-2012 Conference took place in Heidelberg this week – unfortunately I couldn’t attend it. Nevertheless, I received a bit of summary and feedback: The talks will be made available online, I am looking forward to check out a few of them (i.e. Jim Robinson, Jernej Ule). Ivet Bahar (ProDy) and Valerie Daggett (Dynameomics) gave an interesting overview on Molecular Dynamics.

The conference was preceeded by a several tutorials on Monday. Among them on was one on Processing.js (which has been mentioned around here a few times before) and one on D3.js. Both are based on JavaScript and generate cool Visualisations for the Web. D3 only recently got onto my radar, it’s document driven approach seems quite powerful. So it’s definitely worth a look –

see some more examples (like the force-directed layout on the right) on http://mbostock.github.com/d3/ and the workshop slides can be found at http://bost.ocks.org/mike/d3/workshop/.

Thanks to to Corinna Vehlow for feedback!

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