With the data deluge making peta-bytes available, companies and science teams are struggling today to make sense of big data, and to extract value from it. We can model a significant amount of this data as graphs (or “networks”), in which nodes are entities and links are whatever connect them one another. You have already encountered graphs, and you rely on them in real-world situations: communication networks (social networks, web, internet), transportation networks, biological networks (food chain, gene-protein interactions)… to cite but a few examples.
Graph databases allow organizations to understand not just the data, but the relationships within the data. Today, many companies are using graph databases as a foundation for exciting social applications, recommendation engines, fraud detection systems, network & data center management solutions and much more. We believe that graph databases are the best way to work with highly connected data but they can sometimes be difficult to explore. We benefit from Sébastien Heymann’s experience as co-founder of Gephi, a scientific software application to visualize large networks, and from our collaboration with Stanford Mapping the Republic of Letters and DensityDesign in building Knot, a prototype to explore humanities data. We are now proud to introduce Linkurious. At Linkurious SAS, our mission is to help users access and navigate graph databases in a simple manner so they can make sense of their data.
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Graph visualization options and latest developments
InternetActu [FR, March 6, 2013] – De la “Data Science” à l’infovisualisation (1/2) : qu’est-ce qu’un data scientist ?