Using Adamic-Adar Index Algorithm to Predict Volunteer Collaboration: Less is More

TL;DR

This study compares graph-specific algorithms and machine learning methods for predicting volunteer collaborations during COVID-19, finding that the Adamic-Adar Index outperforms other algorithms in this context.

Contribution

The paper demonstrates that the Adamic-Adar Index algorithm surpasses traditional graph algorithms and machine learning models in predicting collaborations in social networks.

Findings

Adamic-Adar Index outperformed Jaccard Coefficient and common neighbour centrality.

Graph-specific algorithms can be more effective than machine learning in certain link prediction tasks.

The study provides insights into social network analysis during pandemic volunteer activities.

Abstract

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.