Graph Neural Networks for Social Network Analysis: Early Concepts and Applications

Abstract

Graph Neural Networks (GNNs) represent a transformative approach to analyzing
graph-structured data, such as social networks. This paper reviews the foundational methods
and applications of GNNs for social network analysis before 2013. It examines their use in
community detection, link prediction, and influence propagation, comparing them to traditional
graph-based techniques like PageRank and spectral clustering. Experiments on benchmark
social network datasets highlight the potential of GNNs to uncover complex relationships and
predict dynamic behaviors. Despite early limitations in scalability and computational efficiency,
GNNs demonstrated promising results, setting the stage for future advancements in social
network analysis.

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