Advances in Network Reconstruction


Networks of influence are found at all levels of physical, biological, and societal systems: climate networks, gene networks, neural networks, and social networks are a few examples. These networks are not just descriptive of the “State of Nature”, they allow us to make predictions such as forecasting disruptive weather patterns, evaluating the possible effect of a drug, locating the focus of a neural seizure, and predicting the propagation of epidemics. This, in turns, allows us to device adequate interventions or change in policies to obtain desired outcomes: evacuate people before a region is hit by a hurricane, administer treatment, vaccinate, etc. But knowing the network structure is a prerequisite, and this structure may be very hard and costly to obtain with traditional means. For example, the medical community relies on clinical trials, which cost millions of dollars; the neuroscience community engages in connection tracing with election microscopy, which take years before establishing the connectivity of 100 neurons (the brain contains billions). These classes will review recent progresses that have been made in network reconstruction methods based solely on observational data. Great advances have been recently made using machine learning. We will analyze the results of several challenges we organized, which point us to new simple and practical methodologies to uncover potential cause-effect relationships and to prioritize experiments.