Unsupervised learning of latent edge types from multi-relational data

Many relational datasets, including relational databases, feature links of different types (e.g., actors act in movies, users rate movies), known as multi-relational, heterogeneous, or multilayer networks. Edge types/network layers are often not explicitly labeled, even when they influence the underlying graph generation process. For example, IMDb lists Tom Cruise as a cast member of Mission Impossible, but not as its star. Inferring latent layers is useful for relational prediction tasks (e.g., predict Tom Cruise's salary or his presence in other movies). This thesis discusses Latent Layer Generative Framework - LLGF, a generative framework for learning latent layers that generalizes Variational Graph Auto-Encoders (VGAEs) with arbitrary node representation encoders and link generation decoders. The decoder treats the observed edge type signal as a linear combination of latent layer decoders. The encoder infers parallel node representations, one for each latent layer. We evaluate our proposed framework, LLGF, on eight benchmark graph learning datasets for this study. Four of the datasets are heterogeneous (originally labeled with edge types); we apply LLGF after removing the edge labels to assess how well it recovers ground-truth layers. LLGF increases link prediction accuracy, especially for heterogeneous datasets (up to 5% AUC), and recovers the ground-truth layers exceptionally well.