提出新型边级图指导网络，提升复杂图结构上的多任务回归性能。
Edge-Wise Graph-Instructed Neural Networks
- 采用边级注意力机制动态调整信息传递，更精细建模图结构。
- 在巴比重模型和埃杜斯-雷尼模型上均实现更低误差率。
- 适合处理连通性混乱的复杂图数据，如社交网络或生物网络。
多任务节点回归问题近年来通过图指导神经网络（GINN）得到解决，其属于消息传递图神经网络的一个子集。本文探讨了图指导（GI）层的局限性，并提出一种新型的边级图指导（EWGI）层。我们分析了该层的优势，并提供了数值证据表明，与传统GINN相比，基于EWGI的网络在从巴比重模型生成的图数据上表现更优；同时在连通性混沌的埃杜斯-雷尼模型图上，也展现出更好的训练正则化效果。
原文摘要 · Abstract (English)
The problem of multi-task regression over graph nodes has been recently approached through Graph-Instructed Neural Network (GINN), which is a promising architecture belonging to the subset of message-passing graph neural networks. In this work, we discuss the limitations of the Graph-Instructed (GI) layer, and we formalize a novel edge-wise GI (EWGI) layer. We discuss the advantages of the EWGI layer and we provide numerical evidence that EWGINNs perform better than GINNs over some graph-structured input data, like the ones inferred from the Barabasi-Albert graph, and improve the training regularization on graphs with chaotic connectivity, like the ones inferred from the Erdos-Renyi graph.
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