Insights from Network Science can advance Deep Graph Learning

TL;DR

The paper argues that deep graph learning and network science have grown apart despite sharing graph-structured data, and it outlines a structured path to bridge them. It proposes leveraging probabilistic ensembles, principled pooling, higher-order and temporal modeling, and continuous, interpretable architectures to improve robustness and generalization. It also discusses practical challenges in scalability, evaluation, and culture, calling for standardized benchmarks and cross-disciplinary curricula. By combining the interpretability of network-science methods with the scalability of deep learning, the work envisions principled, scalable, foundation-like graph representations applicable across domains.

Abstract

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.

Figures (1)

• Figure 1: Illustrative example of how network science insights can advance deep graph learning. Deep graph learning provides tools for end-to-end representations learning for prediction tasks (top panel), while network science provides advanced statistical modeling techniques for handling noisy graph data (bottom panel). Applying deep graph learning directly to noisy co-occurrence data (left) results in a latent space representation that poorly reflects ground truth node labels (top panel). Network science modeling techniques---such as statistical ensembles of random graphs that preserve aggregate characteristics of the data---help build robust graph models that account for noise (bottom panel). Combining these techniques with deep learning methods leads to a latent space representation that better captures ground truth patterns (bottom right). Figure partly adapted from casiraghi2017.