Using Adamic-Adar Index Algorithm to Predict Volunteer Collaboration:
  Less is More
Chao Wu Peng Chen Baiqiao Yin Zijuan Lin Chen Jiang Di Yu Changhong Zou Chunwang Lui
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.