Graph Neural Networks (GNN) have emerged as a popular and standard approach for learning from graph-structured data. The literature on GNN highlights the potential of this evolving research area and its widespread adoption in real-life applications. However, most of the approaches are either new in concept or derived from specific techniques. Therefore, the potential of more than one approach in hybrid form has not been studied extensively, which can be well utilized for sequenced data or static data together. We derive a hybrid approach based on two established techniques as generalized aggregation networks and topology adaptive graph convolution networks that solve our purpose to apply on both types of sequenced and static nature of data, effectively. The proposed method applies to both node and graph classification. Our empirical analysis reveals that the results are at par with literature results and better for handwritten strokes as sequenced data, where graph structures have not been explored.

翻译：图神经网络（GNN）已成为从图结构数据中学习的一种流行且标准的方法。关于GNN的文献突显了这个不断发展的研究领域的潜力及其在现实应用中的广泛采用。然而，大多数方法要么在概念上是新的，要么源自特定技术。因此，混合形式中多种方法的潜力尚未得到广泛研究，而这种潜力可被有效用于序列数据或静态数据。我们基于两种成熟技术——广义聚合网络和拓扑自适应图卷积网络——提出了一种混合方法，有效解决了在序列数据和静态数据两种类型上的应用需求。所提出的方法同时适用于节点分类和图分类。我们的实证分析表明，结果与文献结果相当，并且在作为序列数据的手写笔画上表现更优，而这类图结构尚未被探索过。