Prof. Petko Bogdanov presented a talk on "Treating time as a first-class citizen in dynamic graph mining" hosted by Boleslaw Szymanski at the joint colloquium of CS Department and NEST Center

Prof. Petko Bogdanov of University at Albany, SUNY presented a talk entitled "Treating time as a first-class citizen in dynamic graph mining" hosted by Boleslaw Szymanski at the joint colloquium of Department of Computer Science and NEST Center on October 29, 2018. The talk focused on using dynamic graph mining to elucidate the activity of in-network processes in diverse application domains from social, mobile and communication networks to infrastructure and biological networks. Compared to static graphs, the temporal information of when graph events occur is an important new dimension for improving the quality, interpretation and utility of mined patterns. However, mining dynamic graphs poses an important, though often overlooked, challenge: observed data must be analyzed at an appropriate temporal resolution (timescale), commensurate with the underlying rate of application-specific processes. If the temporal resolution is too high, evidence for ongoing processes may be fragmented in time; if it is too low, data relevant to multiple ongoing processes may be mixed, thus obstructing discovery. Existing approaches for dynamic graph mining typically adopt a fixed timescale (e.g., minutes, days, years), and they mine for patterns in the corresponding aggregated graph snapshots. However, timescale-aware methods must consider non-uniform resolution across both time and the graph, and thus, account for heterogeneous network processes evolving at varying rates in different graph regions. In this talk I will discuss our recent works on detecting an appropriate timescale for community activity and information propagation in socials networks.