Deep Neural Matching Models for Graph Retrieval

• URL: http://arxiv.org/abs/2110.00925v1
• Date: Sun, 3 Oct 2021 05:34:46 GMT
• Title: Deep Neural Matching Models for Graph Retrieval
• Authors: Chitrank Gupta, Yash Jain
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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