Odin: Multi-Signal Graph Intelligence for Autonomous Discovery in Knowledge Graphs

TL;DR

Odin is a pioneering graph intelligence engine that autonomously discovers meaningful patterns in knowledge graphs by integrating multiple signals and guiding exploration, with proven deployment in regulated industries like healthcare and insurance.

Contribution

It introduces Odin, the first production-deployed autonomous discovery system for knowledge graphs, combining multi-signal scoring and theoretical analysis to improve pattern discovery and efficiency.

Findings

Achieves high recall with efficient beam search exploration.

Demonstrates deployment in healthcare and insurance sectors.

Significantly improves pattern discovery quality and analyst efficiency.

Abstract

We present Odin, the first production-deployed graph intelligence engine for autonomous discovery of meaningful patterns in knowledge graphs without prior specification. Unlike retrieval-based systems that answer predefined queries, Odin guides exploration through the COMPASS (Composite Oriented Multi-signal Path Assessment) score, a novel metric that combines (1) structural importance via Personalized PageRank, (2) semantic plausibility through Neural Probabilistic Logic Learning (NPLL) used as a discriminative filter rather than generative model, (3) temporal relevance with configurable decay, and (4) community-aware guidance through GNN-identified bridge entities and inter-community affinity scores. This multi-signal integration, particularly the bridge scoring mechanism, addresses the "echo chamber" problem where graph exploration becomes trapped in dense local communities. We formalize the autonomous discovery problem, prove theoretical properties of our scoring function, and demonstrate that beam search with multi-signal guidance achieves $O(b \cdot h)$ complexity while maintaining high recall compared to exhaustive exploration. To our knowledge, Odin represents the first autonomous discovery system deployed in regulated production environments (healthcare and insurance), demonstrating significant improvements in pattern discovery quality and analyst efficiency. Our approach maintains complete provenance traceability -- a critical requirement for regulated industries where hallucination is unacceptable.