Living Semantic Web
Does the Semantic Web behave like a living system?
Living Semantic Web > Graph Analysis
In order to analyse the Semantic Web graph we obtained, Pajek, a large networks analysis tool, was selected. The RDF N-Triples serialisation was translated to a ‘.net’ Pajek network file. The triples subjects and objects became network nodes connected by directed edges from subject to object. Nodes are identified by their original URIs to allow network construction and the edges are unnamed so duplicated edges are ignored. This is so because we do not need all this information. We are only interested in the network structure.
The Pajek network has 56,592 nodes and 131,130 arcs. Once loaded in Pajek, the available tools were used to obtain the required information about the graph:
- Average degree and degree distribution: using the Net/Partitions/Degree command.
- Clustering factor: using the Net/Vector/ClusteringCoefficients command.
- Average minimum path length: average over a random selection of 20 nodes (using Partition/CreateRandomPartitions and Partition/MakeCluster of size 20) and the averages of their k-neighbours vectors (using the Net/k-Neighbours with the Net/k-Neighbours/FromCluster option).
- Power-law tails exponent: linear regression from the degree distribution using GNUPlot.