Convolutional Neural Networks (CNNs) have recently led to incredible
breakthroughs on a variety of pattern recognition problems. Banks of finite
impulse response filters are learned on a hierarchy of layers, each
contributing more abstract information than the previous layer. The simplicity
and elegance of the convolutional filtering process makes them perfect for
structured problems such as image, video, or voice, where vertices are
homogeneous in the sense of number, location, and strength of neighbors. The
vast majority of classification problems, for example in the pharmaceutical,
homeland security, and financial domains are unstructured. As these problems
are formulated into unstructured graphs, the heterogeneity of these problems,
such as number of vertices, number of connections per vertex, and edge
strength, cannot be tackled with standard convolutional techniques. We propose
a novel neural learning framework that is capable of handling both homogeneous
and heterogeneous data, while retaining the benefits of traditional CNN
successes.
Recently, researchers have proposed variations of CNNs that can handle graph
data. In an effort to create learnable filter banks of graphs, these methods
either induce constraints on the data or require preprocessing. As opposed to
spectral methods, our framework, which we term Graph-CNNs, defines filters as
polynomials of functions of the graph adjacency matrix. Graph-CNNs can handle
both heterogeneous and homogeneous graph data, including graphs having entirely
different vertex or edge sets. We perform experiments to validate the
applicability of Graph-CNNs to a variety of structured and unstructured
classification problems and demonstrate state-of-the-art results on document
and molecule classification problems.

本文介绍了一种基于图卷积神经网络的新型神经学习框架并探讨了其在处理结构化和非结构化分类问题方面的应用，证明该方法在文档和分子分类问题方面具有国际领先水平。