Complex networks are used as an abstraction for systems modeling in physics, biology, sociology, and other areas. We propose an algorithm, named Deep Node Ranking (DNR), based on fast personalized node ranking and raw approximation power of deep learning for learning supervised and unsupervised network embeddings as well as for classifying network nodes directly. The experiments demonstrate that the DNR algorithm is competitive with strong baselines on nine node classification benchmarks from the domains of molecular biology, finance, social media and language processing in terms of speed, as well as predictive accuracy. Embeddings, obtained by the proposed algorithm, are also a viable option for network visualization.
翻译:复杂网络被用作物理学、生物学、社会学和其他领域系统建模的抽象模型,我们建议采用一个算法,名为深节列(DNR),以快速个性化节点排序和深层学习的原始近似功率为基础,用于学习受监督和不受监督的网络嵌入以及直接对网络节点进行分类。实验表明,DNR算法具有竞争力,在分子生物学、金融、社交媒体和语言处理领域的9个节点分类基准以及速度和预测准确性方面有很强的基线。 由拟议的算法获得的嵌入也是网络可视化的一个可行选择。