@article{4782,
  author = {Maleerat Maliyaem},
  title = {Graph Representation Learning and Structural Analysis of Large-Scale Organizational Email Communication Networks},
  journal = {Journal of Networking Technology},
  year = {2026},
  volume = {17},
  number = {3},
  doi = {https://doi.org/10.6025/jnt/2026/17/3/101-135},
  url = {https://www.dline.info/jnt/fulltext/v17n3/jntv17n3_1.pdf},
  abstract = {The rapid expansion of graph-structured data necessitates advanced analytical methods for understanding
complex organizational communication networks. This study presents a comprehensive graph representation
learning framework tailored for large scale organizational email networks, integrating deep graph neural
networks (GNNs), graph autoencoders, and explainable artificial intelligence (XAI). Utilizing the ia-email-
EU-dir dataset, comprising over 265,000 nodes and 420,000 directed edges, we systematically evaluate
classical embedding techniques against advanced GNN architectures. Our proposed hierarchical encoderdecoder
architecture, specifically the Adversarially Regularized Graph Autoencoder (ARGA), demonstrates
superior performance in capturing both local connectivity and global topological dependencies. ARGA
achieved the highest predictive accuracy for link prediction tasks, yielding an Area Under the Curve (AUC) of
0.948 and an Average Precision (AP) of 0.935, alongside the lowest reconstruction loss. Furthermore, the
framework incorporates multiple XAI modules including GNNExplainer, GraphSHAP, and attention weight
visualization to decode latent representations, thereby identifying critical communication hubs, influential
subgraphs, and hidden community structures. By bridging scalable representation learning with model
interpretability, this unified pipeline offers a robust solution for organizational analytics, enabling practical
applications such as anomaly detection, knowledge flow mapping, and hierarchy discovery. Ultimately, the
empirical findings indicate the profound efficacy of adversarially regularized probabilistic models in
deciphering the intricate, evolving dynamics of sparse, scale free enterprise communication networks, paving
the way for trustworthy AI in enterprise decision support and organizational behavior analysis.},
}