CRAFT: Identifying Key Nodes in the Science Communication Community via Counterfactual Fairness

• Published in: IEEE transactions on computational social systems Vol. 12; no. 4; pp. 1473 - 1484
• Main Authors: Jiang, Wenkang, Tang, Qirui, Lin, Lei, Han, Ye, Wang, Runqiang, He, Hongbo
• Format: Journal Article
• Language: English
• Published: IEEE 01.08.2025
• Subjects: Algorithm fairness; causality; Cause effect analysis; Data models; Ethics; Feature extraction; Knowledge engineering; machine learning; Machine learning algorithms; Measurement; Media; Prediction algorithms; Predictive models; science communication; social computing
• ISSN: 2329-924X; 2373-7476
• DOI: 10.1109/TCSS.2024.3501740

The advent of the new media era has greatly changed the original pattern of science communication, and social networks have become a new front to promote the Public Understanding of Science. The integration of social networks and scientific communication has dissolved the central focus of scientific communication, moving toward a more diversified subject centered around the public networks. Due to the special nature of science communication, the key nodes in the community network often play a crucial role. Specifically, by identifying key nodes and analyzing the trend of grouping them into clusters, the stability and robustness of the complex network of science communication can be significantly improved. This is of great significance for expanding the influence of science communication and attracting relevant practitioners to join. However, the sensitive attributes and potential influence factors of community network nodes can lead to deviations in key node mining using existing algorithms. From the perspective of algorithm fairness, we combine graph neural networks and counterfactual theory to address this issue in this work and propose a framework for key node mining for the characteristics of science communication, C ounte R f A ctual Fairness discrimina T or (CRAFT). Experimental results on real data demonstrate the prediction and fairness results and the efficacy of CRAFT in learning causal representations of latent factors.