Mining interesting link formation rules in social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
RAGE - A rapid graphlet enumerator for large networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
EgoNav: exploring networks through egocentric spatializations
Proceedings of the International Working Conference on Advanced Visual Interfaces
The complexity of mining maximal frequent subgraphs
Proceedings of the 32nd symposium on Principles of database systems
| Hi-index | 0.00 |
We present here a method for analyzing the neighborhoods of all the vertices in a large graph. We first give an algorithm for characterizing a simple undirected graph that relies on enumeration of small induced subgraphs. We make a step further in this direction by identifying not only subgraphs but also the positions occupied by the different vertices of the graph, being thus able to compute the roles played by the vertices of the graph. We apply this method to the neighborhood of each vertex in a 2.7M vertices, 6M edges mobile phone graph. We analyze how the contacts of each person are connected to each other and the positions they occupy in the neighborhood network. Then we compare the intensity of their communications (duration and frequency) to their positions, finding that the two are notindependent. We finally interpret and explain the results using social studies on phone communications.