Cooperative Graph Neural Networks
This work addresses the need for more flexible graph learning methods, offering a novel approach that could enhance efficiency and accuracy in domains like social networks or bioinformatics, though it appears incremental as it builds on existing message-passing paradigms.
The authors tackled the problem of rigid message-passing in graph neural networks by introducing a framework where nodes dynamically choose communication strategies, resulting in improved performance on synthetic and real-world datasets.
Graph neural networks are popular architectures for graph machine learning, based on iterative computation of node representations of an input graph through a series of invariant transformations. A large class of graph neural networks follow a standard message-passing paradigm: at every layer, each node state is updated based on an aggregate of messages from its neighborhood. In this work, we propose a novel framework for training graph neural networks, where every node is viewed as a player that can choose to either 'listen', 'broadcast', 'listen and broadcast', or to 'isolate'. The standard message propagation scheme can then be viewed as a special case of this framework where every node 'listens and broadcasts' to all neighbors. Our approach offers a more flexible and dynamic message-passing paradigm, where each node can determine its own strategy based on their state, effectively exploring the graph topology while learning. We provide a theoretical analysis of the new message-passing scheme which is further supported by an extensive empirical analysis on a synthetic dataset and on real-world datasets.