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Figure 2 – uploaded by Ralf Herwig
Figure 2 ‘e 2. (A) Example of a node’s local network connectivity measured with the node degree and the clustering coefficient. Red, node o interest; blue, neighboring nodes; red lines, node interactions with neighbors; blue lines, interactions between neighboring nodes (B) Node degree distribution of the human protein-protein interaction network derived from high quality interactions of the Consen susPathDB. The node distribution (in log-log scale) follows a power law distribution y=ax’ with parameters a=4896.5 and b=-1.50. (red line). Computational analysis of the network was done with the “network analyzer” function in Cytoscape. (C) Visualization o the protein-protein network consisting of 9 533 proteins with 80 422 interactions using Cytoscape. between two proteins is represented by an edge con- necting the corresponding nodes. The human protein- protein interaction network is fairly large, comprising hundreds of thousands of interactions. Network theory aims at the computation of local and global properties of interaction networks and the deduction of emerg- ing properties that might explain cellular function.” Network theory has a long tradition, and started in the 1950s with the seminal work of Erdész and Rényi,* who invented the framework of random networks, ie, networks where nodes are connected by edges in a ran- dom way. Random networks are not what we observe in biological systems; however, local and global topo- logical measures can be used to distinguish them from real-world networks. One such local measure is, eg, the clustering coefficient, C,, of a node i, which measures its ocal connectedness. The measure evaluates the nodes hat are connected with node i (the neighbors) by divid- ing the number of existing edges between the neighbors of node i by the number of all possible edges between he neighbors (Figure 2A). Another important loca feature is the node degree, D,, of node i, ie, the tota number of edges of this node with other nodes. A globa feature important for real-world graphs is the charac eristic path length, L. This feature measures the aver age shortest path length between all possible pairs o nodes in the network. These characteristics were used in the seminal papers of Watts and Strogatz*! and Bara-
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