Summary of Scalenet: Scale Invariance Learning in Directed Graphs, by Qin Jiang et al.
ScaleNet: Scale Invariance Learning in Directed Graphs
by Qin Jiang, Chengjia Wang, Michael Lones, Yingfang Yuan, Wei Pang
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a new approach to node classification in Graph Neural Networks (GNNs) by extending the concept of scale invariance, commonly used in image processing. The proposed method, called “scaled ego-graphs,” replaces single-edges with ordered sequences of directed edges, allowing for more accurate and efficient node classification. Experimental results show that this approach outperforms inception models derived from random walks on both homophilic and heterophilic graph structures. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Node classification in GNNs is a crucial task in relational data analysis, but current methods lack the ability to capture multi-scale features. This research introduces the concept of “scaled ego-graphs” which generalizes traditional ego-graphs by replacing single-edges with ordered sequences of directed edges. The proposed method achieves state-of-the-art results on five out of seven benchmark datasets and demonstrates its applicability across various graph types. |
Keywords
* Artificial intelligence * Classification