Year-end Sale % OFF 
Abstract
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.
Highlights
M OST entities in nature are related to each other, interacting based on their relationship, and changing their states over time based on such interactions
We propose a DiScovering Latent Relation (DSLR) model composed of two graph neural networks (GNNs) [6]–[9]
The DSLR model is trained without supervision of the relation states in an end-to-end manner, which is optimized based on four loss functions, i.e., the node prediction loss, KL divergence loss, relation standard deviation loss, and relation centrality loss
Summary
M OST entities in nature are related to each other, interacting based on their relationship, and changing their states over time based on such interactions. A dynamic NRI (dNRI) model [5], a method that can better infer the changing relations between entities in an interaction system, was developed These NRI-based methods can discover relations between entities and predict the future states of such entities in various interacting systems. Because these methods are designed to handle each relation type with a dedicated update function, they may be difficult to apply to systems with unknown or large numbers of relationships. The first component of the DSLR is the relation encoder (see Fig. 2 (b)), which is a network that infers the relations between nodes by observing the states of the nodes (entities) in interacting systems for a certain period of time. J, i=j where L represents a function that predicts change in the node states
Sign-in/Register to view highlights & summary 
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
