Discovering Latent Representations of Relations for Interacting Systems

TL;DR

The paper introduces DSLR, a flexible model for discovering latent relations in interacting systems, capable of handling unknown and complex relation types in dynamic graphs.

Contribution

It proposes a novel encoder-decoder framework that represents relations in a latent space, enabling analysis of systems with complex and unknown relations.

Findings

Effective on synthetic and real-world data

Handles many relation types and unknown relation counts

Outperforms existing methods in dynamic graph analysis

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.