In many complex systems, the exact relationship between entities is unknown and unobservable. Instead, we may observe interdependent signals from the nodes, such as time series. Current methods for detecting communities (i.e. nodes that are more closely related to one another than to the rest of the network) when edges are unobservable typically involve a complicated process: choose a measure to assess the similarity between pairs of time series, convert the similarity matrix to a (weighted) network, and, finally, infer community structure. This approach is computationally expensive and each step of the three-stage process computes point estimates, making it difficult to distinguish genuine structure from noise.
|Discovering clusters in financial time series|
We propose a Bayesian hierarchical model for multivariate time series data that provides an end-to-end community detection algorithm and propagates uncertainties directly from the raw data to the community labels. Our approach naturally supports multiscale community detection and enables community detection even for short time series. We uncover salient communities in both financial returns time series of S&P100 stocks and climate data in the United States. You can read the article for free here in Science Advances under the title Community detection in networks without observing edges. This was fun work with our collaborators Leto Peel and Renaud Lambiotte. Till and Nick