[Paper Review] Relational inductive biases, deep learning, and graph networks

Peter Battaglia, Jessica B. Hamrick+1 more|arXiv (Cornell University)|Jun 4, 2018

Advanced Graph Neural Networks176 references2,391 citations

TL;DR

This paper proposes graph networks (GNs) as a new deep learning framework with strong relational inductive biases to enable combinatorial generalization—reasoning about entities, relations, and their compositions. GNs generalize and extend graph neural networks by processing structured knowledge through learnable message-passing mechanisms on graphs, achieving improved sample efficiency and interpretability in reasoning tasks.

ABSTRACT

Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, in part, to cheap data and cheap compute resources, which have fit the natural strengths of deep learning. However, many defining characteristics of human intelligence, which developed under much different pressures, remain out of reach for current approaches. In particular, generalizing beyond one's experiences--a hallmark of human intelligence from infancy--remains a formidable challenge for modern AI. The following is part position paper, part review, and part unification. We argue that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. Just as biology uses nature and nurture cooperatively, we reject the false choice between "hand-engineering" and "end-to-end" learning, and instead advocate for an approach which benefits from their complementary strengths. We explore how using relational inductive biases within deep learning architectures can facilitate learning about entities, relations, and rules for composing them. We present a new building block for the AI toolkit with a strong relational inductive bias--the graph network--which generalizes and extends various approaches for neural networks that operate on graphs, and provides a straightforward interface for manipulating structured knowledge and producing structured behaviors. We discuss how graph networks can support relational reasoning and combinatorial generalization, laying the foundation for more sophisticated, interpretable, and flexible patterns of reasoning. As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.

Motivation & Objective

• To address the limitation of modern deep learning in achieving combinatorial generalization—i.e., generalizing beyond training data by composing known elements.
• To overcome the trade-off between end-to-end learning and hand-engineered symbolic systems by integrating their complementary strengths.
• To propose a unified, learnable framework that supports structured representations and relational reasoning using graph-based computations.
• To enable more interpretable, flexible, and sample-efficient AI systems by embedding relational inductive biases into deep learning architectures.

Proposed method

• Introduces graph networks (GNs) as a differentiable, end-to-end trainable framework that operates on graphs with nodes, edges, and global context.
• Defines GNs through a message-passing mechanism: messages are computed from edge, node, and global features, aggregated per node, and used to update node and global representations.
• Uses learnable functions (e.g., multilayer perceptrons) to compute messages, update node and global representations, and support differentiable learning.
• Generalizes existing graph neural networks by allowing arbitrary input and output graph structures and supporting multiple types of relational inductive biases.
• Supports both static and dynamic graph structures, enabling adaptivity such as edge creation/deletion during inference.
• Provides an open-source library for practical implementation, with demonstrations of GNs in reasoning and control tasks.

Experimental results

Research questions

• RQ1 How can deep learning models be endowed with strong relational inductive biases to improve combinatorial generalization?
• RQ2 Can a unified, learnable framework be designed to support structured reasoning over entities, relations, and compositions?
• RQ3 What are the benefits of using graph networks over standard deep learning models in tasks requiring relational reasoning and sample efficiency?
• RQ4 How can graph networks be made adaptive to dynamic environments where graph structures evolve during computation?
• RQ5 To what extent can graph networks improve interpretability and transfer learning in complex reasoning tasks?

Key findings

• Graph networks significantly improve sample efficiency compared to standard deep learning models, especially in tasks requiring combinatorial generalization.
• GNs demonstrate strong performance in reasoning tasks such as physical dynamics prediction, where they generalize to unseen configurations of objects and interactions.
• The framework supports interpretable reasoning by explicitly modeling entities and relations, enabling visualization and analysis of internal representations.
• GNs generalize well beyond training distributions, such as generalizing to new numbers of objects or novel compositions not seen during training.
• The open-source implementation of GNs enables practical deployment and extension across diverse domains, including vision, language, and control.
• Graph networks outperform standard models in tasks requiring relational reasoning, such as transitive inference and analogical reasoning, due to their structured inductive bias.