Graph heat mixture model learning
  Graph inference methods have recently attracted a great interest from the
scientific community, due to the large value they bring in data interpretation
and analysis. However, most of the available state-of-the-art methods focus on
scenarios where all available data can be explained through the same graph, or
groups corresponding to each graph are known a priori. In this paper, we argue
that this is not always realistic and we introduce a generative model for mixed
signals following a heat diffusion process on multiple graphs. We propose an
expectation-maximisation algorithm that can successfully separate signals into
corresponding groups, and infer multiple graphs that govern their behaviour. We
demonstrate the benefits of our method on both synthetic and real data.
