GraphInterpreter: a visual analytics approach for dynamic networks evolution exploration via topic models

We propose a novel visual analytics approach based on the Latent Dirichlet Allocation (LDA) model for exploring and interpreting the dynamic evolution of networks. In this approach, we define networks as documents and relationships within networks as words. Using this definition, the LDA model is able to extract a list of structures that fuse relationships and connect the network features. We project networks described by the extracted structures with probabilistic assignments as points into a two-dimensional space via dimensionality reduction techniques. Users can identify evolution states in dynamic networks, including stable states, recurrent states, outlier states, and state transitions. To facilitate the interpretation of evolution states, we provide a novel small multiples view that shows how the extracted structures behave over time. We demonstrate the effectiveness of our work through case studies conducted on two real-world dynamic networks.