Using Adamic-Adar Index Algorithm to Predict Volunteer Collaboration: Less is More
This work addresses link prediction in social networks for volunteer coordination, but it is incremental as it compares existing algorithms on a new dataset.
The study tackled the problem of predicting volunteer collaborations in social networks during the Covid-19 pandemic in Shenzhen, finding that the Adamic-Adar Index algorithm outperformed other graph-specific and machine learning methods.
Social networks exhibit a complex graph-like structure due to the uncertainty surrounding potential collaborations among participants. Machine learning algorithms possess generic outstanding performance in multiple real-world prediction tasks. However, whether machine learning algorithms outperform specific algorithms designed for graph link prediction remains unknown to us. To address this issue, the Adamic-Adar Index (AAI), Jaccard Coefficient (JC) and common neighbour centrality (CNC) as representatives of graph-specific algorithms were applied to predict potential collaborations, utilizing data from volunteer activities during the Covid-19 pandemic in Shenzhen city, along with the classical machine learning algorithms such as random forest, support vector machine, and gradient boosting as single predictors and components of ensemble learning. This paper introduces that the AAI algorithm outperformed the traditional JC and CNC, and other machine learning algorithms in analyzing graph node attributes for this task.