Empowering Interdisciplinary Insights with Dynamic Graph Embedding Trajectories

Aug 25, 2025·
Yiqiao Jin
Yiqiao Jin
,
Andrew Zhao
,
Yeon-Chang Lee
,
Meng Ye
,
Ajay Divakaran
,
Srijan Kumar
· 1 min read
Abstract
Interdisciplinary research relies on tracking how concepts, communities, and citations evolve over time. We propose Dynamic Graph Embedding Trajectories (DyGET), a framework that converts temporal graph embeddings into interpretable trajectories that surface interdisciplinary insights. DyGET supports cross-disciplinary discovery, anomaly detection, and longitudinal community analysis.
Type
Publication
KDD 2025 Workshop on Temporal Graph Learning (TGL)

Abstract

Interdisciplinary research relies on tracking how concepts, communities, and citations evolve over time. We propose Dynamic Graph Embedding Trajectories (DyGET), a framework that converts temporal graph embeddings into interpretable trajectories that surface interdisciplinary insights.

Yiqiao Jin
Authors
Ph.D. Candidate in Computer Science
My research focuses on adaptive and efficient AI systems, with emphasis on LLM agents, agent memory, self-distillation, multimodal LLMs, and structured multi-agent intelligence.