Given a graph, how can we extract good features for the nodes? For example, given two large graphs from the same domain, how can we use information in one to do classification in the other (i.e., perform across-network classification or transfer learning on graphs)? Also, if one of the graphs is anonymized, how can we use information in one to de-anonymize the other? The key step in all such graph mining tasks is to find effective node features. We propose ReFeX (Recursive Feature eXtraction), a novel algorithm, that recursively combines local (node-based) features with neighborhood (egonet-based) features; and outputs regional features – capturing “behavioral ” information. We demonstrate how these powerful regional features can be used in within-network and across-network classification and de-anonymization tasks – without relying on homophily, or the availability of class labels. The contributions of our work are as follows: (a) ReFeX is scalable and (b) it is effective, capturing regional (“behavioral”) information in large graphs. We report experiments on real graphs from various domains with over 1M edges, where ReFeX outperforms its competitors on typical graph mining tasks like network classification and de-anonymization.

How do opinions get formed in online forums? Which people should we immunize, to prevent an epidemic as fast as possible? My principal long term research interest is to solve large real-world problems, by understanding and managing efficiently, dynamical mechanisms on networks, occurring across natural, social, and technological systems. For example, given some amount of resource like antibiotics, how should one distribute it across hospitals? Figure 1 illustrates my SMART-ALLOC algorithm designed for this purpose: it results in ∼ 6 times fewer hospitals infected nationwide. Each resource roughly halved the susceptibility of a hospital and the same amount (200) were distributed.

How do opinions get formed in online forums? Which people should we immunize, to prevent an epidemic as fast as possible? My principal long term research interest is to solve large real-world problems, by understanding and managing efficiently, dynamical mechanisms on networks, occurring across natural, social, and technological systems. For example, given some amount of resource like antibiotics, how should one distribute it across hospitals? Figure 1 illustrates my SMART-ALLOC algorithm designed for this purpose: it results in ∼ 6 times fewer hospitals infected nationwide. Each resource roughly halved the susceptibility of a hospital and the same amount (200) were distributed.