Madras Labs helps customers to understand networks as graph and each every network can be visually represented in terms of graph theory as nodes (individuals) edges (friendship), adjacency matrix, neighborhood, in-degrees and out-degrees of distribution.
We analyse Simple, directed, undirected and multiple weighted graphs using open source tools like Gephi and identify the connectivity and shortest paths between two different neighbors in a large scale network.Need Demo?
Our approach to identifying customers who are most influential in a network can be quantified in a graph to have a high centrality measure using degree of centrality and identification of eigenvectors. We also do measures for similarity, social balance that explains the interactions between individuals, betweenness and closeness centrality. Some of the properties of network models like Power Law degree distribution and Linear threshold model can help to determine the influencers vs normal ones. we help customers to discover communities using communities detection.Want to see the business value?