Cooperative Graph Neural Networks
Ben Finkelshtein Xingyue Huang Michael Bronstein İsmail İlkan Ceylan
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.