Graph & Network Mining: clustering/community detection/ classification
Well ... Some points. Networked data is modeled with graphs. When you have different attributes you have Property Graph.

For clustering, you can extract the topology of the subgraph based on desired attributes and then use any Modularity-based algorithm (most recommended is Blondel algorithm). In Blondel algorithm you don't need to know the number of communities in advance.

Have a look at Network Science book by Barabasi to get more insight to networks.

For classification you may extract features from graphs and use common classification algorithms or use graph kernels and feed it to kernel methods such as SVM. Follow this.

Page rank is one of the methods for ranking but you have simpler choices according to your problem. See Centrality measures from the book above. There you can also see details of different ranking algorithms.

If you need more info you may drop a comment here.

Hope it helped. Good Luck!