Summary of Unleashing the Potential Of Fractional Calculus in Graph Neural Networks with Frond, by Qiyu Kang and Kai Zhao and Qinxu Ding and Feng Ji and Xuhao Li and Wenfei Liang and Yang Song and Wee Peng Tay
Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND
by Qiyu Kang, Kai Zhao, Qinxu Ding, Feng Ji, Xuhao Li, Wenfei Liang, Yang Song, Wee Peng Tay
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The FRactional-Order graph Neural Dynamical network (FROND) is a new continuous graph neural network framework that leverages the non-local properties of fractional calculus to capture long-term dependencies in feature updates. Unlike traditional continuous GNNs, FROND employs the Caputo fractional derivative, which enables the modeling of non-Markovian update mechanisms. This approach offers enhanced capabilities in graph representation learning and can mitigate oversmoothing. The authors demonstrate the effectiveness of FROND by comparing its performance with various established integer-order continuous GNNs on several benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FROND is a new way to make computer networks learn about patterns in data that are connected. Traditional networks have trouble learning these patterns because they only look at what’s happening right now, not what happened before. FROND can see the past and use that information to make better predictions. This helps the network avoid making mistakes and get better results. The people who made FROND tested it against other networks and showed that it does a better job. |
Keywords
» Artificial intelligence » Graph neural network » Representation learning
