Insights from Network Science can advance Deep Graph Learning
This is an incremental position paper that addresses challenges in both fields, such as data augmentation and scaling, to advance graph analysis for researchers and practitioners.
The paper explores the intersection of network science and deep graph learning, identifying shared goals and untapped potential for integration to better model graph-structured data, with early efforts indicating significant opportunities.
Deep graph learning and network science both analyze graphs but approach similar problems from different perspectives. Whereas network science focuses on models and measures that reveal the organizational principles of complex systems with explicit assumptions, deep graph learning focuses on flexible and generalizable models that learn patterns in graph data in an automated fashion. Despite these differences, both fields share the same goal: to better model and understand patterns in graph-structured data. Early efforts to integrate methods, models, and measures from network science and deep graph learning indicate significant untapped potential. In this position, we explore opportunities at their intersection. We discuss open challenges in deep graph learning, including data augmentation, improved evaluation practices, higher-order models, and pooling methods. Likewise, we highlight challenges in network science, including scaling to massive graphs, integrating continuous gradient-based optimization, and developing standardized benchmarks.