Transfer Learning for Graph Anomaly Detection Using Energy-Based Models
EasyChair Preprint 15464
7 pages•Date: November 24, 2024Abstract
Graph Anomaly Detection (GAD) has applications across social networks, financial systems,
and cyber security. Traditional GAD methods, particularly energy-based models (EBMs),
detect abnormal patterns within graph structures but require extensive training data,
limiting their use on smaller graphs. This paper proposes a novel approach integrating
transfer learning with EBMs to improve anomaly detection performance on graphs with
limited data. The model pre-trains on large source graphs and transfers knowledge to target
graphs with less data, achieving higher accuracy and computational efficiency. We present a
rigorous mathematical foundation and provide detailed experimental results, including
performance metrics, across five tables.
Keyphrases: Algorithms, EBMs, Transfer Learning, graph anomaly detection