Matching Models for Graph Retrieval
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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.
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.
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3 of 7 references.
References · 3
- 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,554
- 2019Graph Matching Networks for Learning the Similarity of Graph Structured Objectscited by 205
- 2018GraKeL: A Graph Kernel Library in Pythoncited by 54
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