(link to download networks) https://snap.stanford.edu/data/ may Explore libaray(igraphdata) to find different networks in R and Python to use https://archive.ics.uci.edu/ml/datasets.html? format=&task=cla&att=&area=&numAtt=&numIns=&type=&sort=nameUp&view=table (tools to analyse data) https://sourceforge.net/projects/socnetv/
- Download a labeled numeric dataset from UCI repository. Compute distances between data points construct a class-wise box plot. Transform the data to graph by using 1st quartile (whole data) of distances as threshold. Find the radius, diameter of the graph. Plot the degree distribution.
- Download a real network and show that it follows all three properties (small world, clustering coefficient and scale free) or not (minimum 1000 nodes)
- Compare the effectiveness of Eigen vector centrality on unweighted and weighted networks
- Compare the communities generated by one hierarchical based, divisive based and modularity based community detection methods using a networks with predefined communities.