Multimedialne seminarium z ekono- i socjofizyki

Long distance connections in the modern interconnected world play an important role in many areas of life, such as information spreading, epidemics, financial contagion or opinion dynamics. This drives the need for proper understanding of diffusion and spreading processes on networks. Another unprecedented feature of the current era is the data availability, which, together with rapid development of machine learning tools, allow us to learn and predict models from observed processes. In reality, however, these large amounts of data are often incomplete, noisy or biased. We address this problem in the case of spreading processes on networks and propose a general framework, which allows us to learn, predict and optimize spreading models from data, when the latter is incomplete or subject to uncertainty. We also describe the general philosophy behind our idea and how it could be used with different inference methods. Additionally, we show how the procedure can be improved when additional information about the process is known.join us via zoomhttps://us02web.zoom.us/j/82025295160?pwd=Rno0Skx6Q3haRzVzZy90dkRrbmxQQT09Meeting ID: 820 2529 5160Passcode: 794269