The non-backtracking property of NBA-GNN can be extended to other graph neural network (GNN) architectures by integrating non-backtracking message-passing mechanisms into their existing frameworks. For instance, architectures like Graph Attention Networks (GATs) or Graph Convolutional Networks (GCNs) can adopt the non-backtracking approach by modifying their message aggregation functions to prevent the incorporation of messages from previously visited nodes. This can be achieved by redefining the aggregation step to only consider messages from neighbors that do not include the source node of the current message.
Additionally, the non-backtracking principle can be applied to recurrent GNNs, where the recurrent update rules can be adjusted to ensure that messages do not backtrack. This could involve designing a new set of update equations that explicitly account for the non-backtracking condition, thereby enhancing the model's ability to capture long-range dependencies without redundancy.
Moreover, the non-backtracking property can be combined with attention mechanisms to create a hybrid model that leverages both the selective focus of attention and the efficiency of non-backtracking updates. This could lead to improved performance in tasks requiring nuanced understanding of graph structures, such as community detection or molecular property prediction.