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Graph Alignment Networks With Node Matching Scores

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Link to Profile Matthias Schubert PI Matchmaking

Matthias Schubert

Prof. Dr.

Principal Investigator

Spatial Artificial Intelligence

Abstract

In this work we address the problem of graph node alignment at the example of Map Fusion (MF). Given two partly overlapping road networks, the goal is to match nodes that represent the same locations in both networks. For this task we propose a new model based on Graph Neural Networks (GNN). Existing GNN approaches, which have recently been successfully applied on various tasks for graph based data, show poor performance for the MF task. We hypothesize that this is mainly caused by graph regions from the non-overlapping areas, as information from those areas negatively affect the learned node representations. Therefore, our model has an additional inductive bias and learns to ignore effects of nodes that do not have a matching in the other graph. Our new model can easily be extended to other graph alignment problems, e.g., for calculating graph similarities, or for the alignment of entities in knowledge graphs, as well.

inproceedings FVB+19

Graph Representation Learning @NeurIPS 2019

Workshop on Graph Representation Learning at the 33rd Conference on Neural Information Processing Systems. Vancouver, Canada, Dec 08-14, 2019.

Authors

E. Faerman • O. Voggenreiter • F. Borutta • T. Emrich • M. Berrendorf • M. Schubert

Links

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Research Area

 A3 | Computational Models

BibTeXKey: FVB+19

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