Dynamically Weighted Graph Convolutional Networks for Adaptive Feature Learning

Abstract

This paper proposes a novel approach to Graph Convolutional Networks (GCNs) that addresses the limitations of static weight assignment in traditional GCNs. We introduce Dynamically Weighted Graph Convolutional Networks (DW-GCNs) which adaptively learn connection weights based on the evolving dynamics of the graph structure and the input features. The core idea is to allow the network to adjust its attention mechanisms, prioritizing more relevant connections at different stages of processing. This dynamic weighting is achieved through a mechanism that implicitly or explicitly learns optimal weights, potentially leveraging reinforcement learning techniques. The resulting DW-GCNs demonstrate improved performance in adaptive feature learning tasks compared to standard GCNs, particularly in scenarios with non-stationary graph structures or varying feature distributions. The key contribution lies in the ability to create a GCN that isn't simply a static aggregation of neighbors but actively responds to the information flow. This work paves the way for more robust and efficient GCN models for various applications, including social network analysis, biological pathway inference, and dynamic sensor networks.