Summary of Mitigating Degree Bias in Signed Graph Neural Networks, by Fang He et al.
Mitigating Degree Bias in Signed Graph Neural Networks
by Fang He, Jinhai Deng, Ruizhan Xue, Maojun Wang, Zeyu Zhang
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Medium Difficulty summary: This paper tackles fairness issues in Signed Graph Neural Networks (SGNNs), which are used to analyze signed graphs that capture both positive and negative relationships between entities. Building on previous work on degree bias, the authors propose a new Model-Agnostic method, called Degree Debiased Signed Graph Neural Network (DD-SGNN), to enhance node representation and mitigate biases in SGNNs. The approach involves transferring information from nodes with high degrees to those with low degrees within a head-to-tail triplet, maintaining positive and negative semantics specified by balance theory. Experimental results on four real-world datasets show that the proposed model effectively reduces degree bias without compromising performance metrics such as AUC and F1. The authors provide code for supplementary materials. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper deals with making computer models fair when they look at networks of relationships between people or things. These networks can be good or bad, like friendships or rivalries. Right now, these models have a problem where they favor some nodes over others just because those nodes are “popular” or well-connected. The authors created a new way to make the models more fair by sharing information from popular nodes with less popular ones. They tested this approach on real-world networks and found that it works! This is important because computer models can be used in many areas, such as deciding who gets hired or which products are recommended. |
Keywords
* Artificial intelligence * Auc * Graph neural network * Semantics
