DeepGAR: Deep Graph Learning for Analogical Reasoning

Publication Date: 12/3/2022

Event: IEEE ICDM 2022 – 22nd IEEE International Conference on Data Mining, Orlando, FL

Reference: pp. 1-6, 2022

Authors: Chen Ling, Emory University; Tanmoy Chowdhury, George Mason University; Junji Jiang, Tianjin University; Junxiang Wang, Emory University; Xuchao Zhang, NEC Laboratories America, Inc.; Haifeng Chen, NEC Laboratories America, Inc.; Liang Zhao, Emory University

Abstract: 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. The code and data are available at:

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