Machine Learning

Graph Convolution: A High-Order and Adaptive Approach

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  • arXiv
    5 pts

    Graph Convolution: A High-Order and Adaptive Approach

    In this paper, we presented a novel convolutional neural network framework for graph modeling, with the introduction of two new modules specially designed for graph-structured data: the $k$-th order convolution operator and the adaptive filtering module. Importantly, our framework of High-order and Adaptive Graph Convolutional Network (HA-GCN) is a general-purposed architecture that fits various applications on both node and graph centrics, as well as graph generative models. We conducted extensive experiments on demonstrating the advantages of our framework. Particularly, our HA-GCN outperforms the state-of-the-art models on node classification and molecule property prediction tasks. It also generates 32% more real molecules on the molecule generation task, both of which will significantly benefit real-world applications such as material design and drug screening.

    Graph Convolution: A High-Order and Adaptive Approach
    by Zhenpeng Zhou, Xiaocheng Li
    https://arxiv.org/pdf/1706.09916v2.pdf

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