Semi-Supervised Graph-to-Graph Translation

TL;DR

This paper introduces a semi-supervised graph translation model that leverages dual representation spaces and specialized encoder/decoder structures to effectively capture semantic changes in graphs, even with limited paired data.

Contribution

It proposes a novel semi-supervised graph translation approach using dual spaces and mutual information loss to improve semantic change modeling with scarce paired datasets.

Findings

Effective in three different datasets

Improves semantic transition modeling in graphs

Utilizes unpaired samples for better generalization

Abstract

Graph translation is very promising research direction and has a wide range of potential real-world applications. Graph is a natural structure for representing relationship and interactions, and its translation can encode the intrinsic semantic changes of relationships in different scenarios. However, despite its seemingly wide possibilities, usage of graph translation so far is still quite limited. One important reason is the lack of high-quality paired dataset. For example, we can easily build graphs representing peoples' shared music tastes and those representing co-purchase behavior, but a well paired dataset is much more expensive to obtain. Therefore, in this work, we seek to provide a graph translation model in the semi-supervised scenario. This task is non-trivial, because graph translation involves changing the semantics in the form of link topology and node attributes, which is difficult to capture due to the combinatory nature and inter-dependencies. Furthermore, due to the high order of freedom in graph's composition, it is difficult to assure the generalization ability of trained models. These difficulties impose a tighter requirement for the exploitation of unpaired samples. Addressing them, we propose to construct a dual representation space, where transformation is performed explicitly to model the semantic transitions. Special encoder/decoder structures are designed, and auxiliary mutual information loss is also adopted to enforce the alignment of unpaired/paired examples. We evaluate the proposed method in three different datasets.