Node Classification in Weighted Complex Networks Using Neighborhood Feature Similarity

The potential of graph representation learning schemes has attained great acceptance in diverse, complex network applications. Most of the existing Graph Neural Network (GNN) architectures explore the node features aggregation and feature transformation within the neighborhoods, mainly performed on the unweighted graphs. Also, the existing GNN architectures consider all sets of neighborhood features, which are computationally expensive tasks. Practically, most of the real-world graphs are weighted graphs, and it is important to learn the representation of weighted graphs. In this work, we generate and leverage information of the best possible feature combination from the multiple levels of the networks. Edge weights and the connection structure are considered for generating node embedding, and classifying the node more accurately. The proposed framework, Similarity Feature Embedding GNN (SFEGNN), can be efficiently used for node classification in the weighted networks by leveraging feature overlap similarity from the network structure. This novel approach is helpful in modeling weighted networks for node classification and determining how strongly the neighborhood features are correlated. We validate the efficacy of SFEGNN on six benchmark datasets with varying degrees of homophily ratio and found that it is effective even for highly heterophily networks. Our model has empirically outperformed the state-of-the-art GNN framework with the highest accuracy improvement of 28.88%.