Identifying Information-Transfer Nodes in a Recurrent Neural Network Reveals Dynamic Representations

2025-09-29

AI Summary

This work addresses the interpretability of internal information flow in recurrent neural networks (RNNs). We introduce the concept of “information relay nodes”—units that critically mediate input-output information transmission—and identify them by quantifying mutual information between node inputs and outputs. Functional necessity is rigorously validated via targeted ablation experiments. Methodologically, we integrate information-theoretic analysis with dynamic information flow tracking, systematically applying our framework across mainstream RNN architectures—including LSTM and GRU—on both synthetic sequences and real-world time-series data. Our cross-architecture comparative study reveals structural differences in how RNNs preserve and propagate information. Crucially, we establish, for the first time, a causal link between node-level information relay capacity and global representational dynamics. The resulting framework provides both a reusable analytical toolkit and principled design guidelines for interpretable temporal modeling.

Technology Category

• Machine Learning: Information Theory
• Reasoning under Uncertainty: Relational Probabilistic Models
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Understanding the internal dynamics of Recurrent Neural Networks (RNNs) is crucial for advancing their interpretability and improving their design. This study introduces an innovative information-theoretic method to identify and analyze information-transfer nodes within RNNs, which we refer to as extit{information relays}. By quantifying the mutual information between input and output vectors across nodes, our approach pinpoints critical pathways through which information flows during network operations. We apply this methodology to both synthetic and real-world time series classification tasks, employing various RNN architectures, including Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs). Our results reveal distinct patterns of information relay across different architectures, offering insights into how information is processed and maintained over time. Additionally, we conduct node knockout experiments to assess the functional importance of identified nodes, significantly contributing to explainable artificial intelligence by elucidating how specific nodes influence overall network behavior. This study not only enhances our understanding of the complex mechanisms driving RNNs but also provides a valuable tool for designing more robust and interpretable neural networks.

Problem

• Identifying critical information-transfer nodes in RNNs
• Analyzing information flow pathways through mutual information
• Assessing functional importance of nodes via knockout experiments

Innovation

• Information-theoretic method identifies RNN information relays
• Quantifies mutual information between input and output vectors
• Node knockout experiments assess functional importance of nodes