Discovering Latent Representations of Relations for Interacting Systems
  Systems whose entities interact with each other are common. In many
interacting systems, it is difficult to observe the relations between entities
which is the key information for analyzing the system. In recent years, there
has been increasing interest in discovering the relationships between entities
using graph neural networks. However, existing approaches are difficult to
apply if the number of relations is unknown or if the relations are complex. We
propose the DiScovering Latent Relation (DSLR) model, which is flexibly
applicable even if the number of relations is unknown or many types of
relations exist. The flexibility of our DSLR model comes from the design
concept of our encoder that represents the relation between entities in a
latent space rather than a discrete variable and a decoder that can handle many
types of relations. We performed the experiments on synthetic and real-world
graph data with various relationships between entities, and compared the
qualitative and quantitative results with other approaches. The experiments
show that the proposed method is suitable for analyzing dynamic graphs with an
unknown number of complex relations.
