Learning to Rank from Concept-Drifting Network Data Streams

Networked data are, nowadays, collected in various application domains such as social networks, biological networks, sensor networks, spatial networks, peer-to-peer networks etc. Recently, the application of data stream mining to networked data, in order to study their evolution over time, is receiving increasing attention in the research community. Following this main stream of research, we propose an algorithm for mining ranking models from networked data which may evolve over time. In order to properly deal with the concept drift problem, the algorithm exploits an ensemble learning approach which allows us to weight the importance of learned ranking models from past data when ranking new data. Learned models are able to take the network autocorrelation into account, that is, the statistical dependency between the values of the same attribute on related nodes. Empirical results prove the effectiveness of the proposed algorithm and show that it performs better than other approaches proposed in the literature.