# Matching Models for Graph Retrieval

arXiv: https://arxiv.org/abs/2110.00925 · HTML: https://mlsanity.com/paper/2110.00925/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 3 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Graph Matching Networks for Learning the Similarity of Graph Structured Objects](https://mlsanity.com/paper/1904.12787/) (2019)
- [GraKeL: A Graph Kernel Library in Python](https://mlsanity.com/paper/1806.02193/) (2018)

## Abstract (arXiv)

Graph Retrieval has witnessed continued interest and progress in the past few years. In thisreport, we focus on neural network based approaches for Graph matching and retrieving similargraphs from a corpus of graphs. We explore methods which can soft predict the similaritybetween two graphs. Later, we gauge the power of a particular baseline (Shortest Path Kernel)and try to model it in our product graph random walks setting while making it more generalised.