  Electronic Discovery (eDiscovery) involves identifying relevant documents
from a vast collection based on legal production requests. The integration of
artificial intelligence (AI) and natural language processing (NLP) has
transformed this process, helping document review and enhance efficiency and
cost-effectiveness. Although traditional approaches like BM25 or fine-tuned
pre-trained models are common in eDiscovery, they face performance,
computational, and interpretability challenges. In contrast, Large Language
Model (LLM)-based methods prioritize interpretability but sacrifice performance
and throughput. This paper introduces DISCOvery Graph (DISCOG), a hybrid
approach that combines the strengths of two worlds: a heterogeneous graph-based
method for accurate document relevance prediction and subsequent LLM-driven
approach for reasoning. Graph representational learning generates embeddings
and predicts links, ranking the corpus for a given request, and the LLMs
provide reasoning for document relevance. Our approach handles datasets with
balanced and imbalanced distributions, outperforming baselines in F1-score,
precision, and recall by an average of 12%, 3%, and 16%, respectively. In an
enterprise context, our approach drastically reduces document review costs by
99.9% compared to manual processes and by 95% compared to LLM-based
classification methods
