Deep Learning of Graphs with Ngram Convolutional Neural Networks (Extended Abstract)

Abstract

NgramCNN is a deep convolutional neural network developed for classification of graphs based on common substructure patterns and their latent relationships in the collection of graphs. Our NgramCNN deep learning framework consists of three novel components: (1) The concept of n-gram graph block to transform each raw graph object into a sequence of n-gram blocks connected through overlapping regions. (2) The diagonal convolution layer to extract local patterns and connectivity features hidden in the n-gram blocks by performing n-gram normalization before conducting deep learning through the network of convolution layers. (3) The extraction of deeper global patterns based on the local patterns and the ways that they respond to overlapping regions by building a n-gram deep convolutional neural network. Extensive evaluation of NgramCNN using five real graph repositories from bioinformatics and social networks domains show the effectiveness of NgramCNN over the existing state of art methods with high accuracy and comparable performance.