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Gab is an online social network often associated with the alt-right political movement and users barred from other networks. In this talk, we investigate the rise of this platform and the evolution of user behavior by modeling the graph of user interactions over time. We perform temporal analysis of the graph both across time and at different timescales (e.g. considering data as windows of one day, week or month of interactions). This analysis is enabled by using the distributed processing system Raphtory, which allows us to apply several sliding windows to the temporal graph. This software gives a novel perspective on social network data and allows new insights that would be hard to obtain efficiently using standard tools. As a result of this analysis we identify plausible events that are of interest to the Gab community associated with abnormal interaction bursts and rapid short term growth in the network. The network is unusual in several respects. It grows in a bursty manner that seems to be driven by external events. The network is characterised by interactions between ‘strangers’ rather than by reinforcing links between ‘friends’. Network analysis at Gab usage follows the diurnal cycle of the predominantly US and Europe based users. At off-peak hours the Gab interaction network fragments into sub-networks with absolutely no interaction between them. A small group of users are highly influential across larger timescales, but a substantial number of users gain influence for short periods of time. Temporal graph analysis at different timescales is a rarely used tool that gives considerable insight into online social networks, for example, identifying users and times where interaction characteristics are anomalous.