Large networks are being generated by applications that keep track of relationships between different data entities. Examples include online social networks recording interactions between individuals, sensor networks logging information exchanges between sensors, and more. There is a large body of literature on mining large networks, but most existing methods assume either static networks, or dynamic networks where the network topology is changing. On the other hand, in many real-world applications a continuous stream of interactions takes place on top of a relatively stable network topology, giving rise to different semantics than those of dynamic networks. In this talk we discuss a few different problems that consider networks as a stream of interactions (edges) over time. In particular, we consider the problems of (i) maintaining neighborhood profiles, (ii) tracking important nodes, and (iii) identifying the starting nodes and most-likely flow of an epidemic. For the studied problems we present new algorithms, and discuss our analytical results. We also present experimental evaluation on real-world datasets and case studies on different application scenarios.
Aristides Gionis is an associate professor in the department of Computer Science in Aalto University. Previously he has been a senior research scientist in Yahoo! Research. He is currently serving as an associate editor in the ACM Transactions on Knowledge Discovery from Data (TKDD) and as a managing editor in Internet Mathematics. He has contributed in several areas of data science, such as graph mining, social-media analysis, web mining, data clustering, and privacy-preserving data mining.