# DeepGAR: Deep Graph Learning for Analogical Reasoning

arXiv: https://arxiv.org/abs/2211.10821 · HTML: https://mlsanity.com/paper/2211.10821/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 19 Nov 2022.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Neural Subgraph Matching](https://mlsanity.com/paper/2007.03092/) (2020)
- [Neural Analogical Matching](https://mlsanity.com/paper/2004.03573/) (2020)

## Built on by (most cited first, 5 of 5)

- [Deep Graph Representation Learning and Optimization for Influence Maximization](https://mlsanity.com/paper/2305.02200/) (2023)
- [MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization](https://mlsanity.com/paper/2402.16898/) (2024)
- [A Context-Enhanced Framework for Sequential Graph Reasoning](https://mlsanity.com/paper/2412.09056/) (2024)
- [Transformer See, Transformer Do: Copying as an Intermediate Step in Learning Analogical Reasoning](https://mlsanity.com/paper/2604.06501/) (2026)
- [PRISM: A Category-Theoretic Framework for Measuring and Refining Multimodal Analogies](https://mlsanity.com/paper/2610.01383/) (2026)

## Abstract (arXiv)

Analogical reasoning is the process of discovering and mapping correspondences from a target subject to a base subject. As the most well-known computational method of analogical reasoning, Structure-Mapping Theory (SMT) abstracts both target and base subjects into relational graphs and forms the cognitive process of analogical reasoning by finding a corresponding subgraph (i.e., correspondence) in the target graph that is aligned with the base graph. However, incorporating deep learning for SMT is still under-explored due to several obstacles: 1) the combinatorial complexity of searching for the correspondence in the target graph; 2) the correspondence mining is restricted by various cognitive theory-driven constraints. To address both challenges, we propose a novel framework for Analogical Reasoning (DeepGAR) that identifies the correspondence between source and target domains by assuring cognitive theory-driven constraints. Specifically, we design a geometric constraint embedding space to induce subgraph relation from node embeddings for efficient subgraph search. Furthermore, we develop novel learning and optimization strategies that could end-to-end identify correspondences that are strictly consistent with constraints driven by the cognitive theory. Extensive experiments are conducted on synthetic and real-world datasets to demonstrate the effectiveness of the proposed DeepGAR over existing methods.