Preferential Attachment

Preferential attachment describes the phenomenon where nodes in a network preferentially connect to already well-connected nodes, leading to the "rich-get-richer" effect. Current research focuses on understanding and mitigating biases stemming from this effect in various applications, including link prediction in graph neural networks and fake account detection on social media. This involves developing new models, such as those incorporating heterogeneous reciprocity or multi-class classifications, and analyzing the fairness and accuracy of existing algorithms. The insights gained are crucial for improving network analysis, developing more equitable algorithms, and enhancing the robustness of online platforms.