Graph heat mixture model learning
This addresses a limitation in graph inference for scenarios where data groups are not pre-defined, which is incremental but useful for data analysis in fields like network science.
The paper tackles the problem of inferring multiple graphs from mixed signals when groups are unknown, introducing a generative model based on heat diffusion and an expectation-maximisation algorithm to separate signals and infer graphs, demonstrating benefits on synthetic and real data.
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.