GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks
Sukhdeep Singh Anuj Sharma Vinod Kumar Chauhan
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.