使用深度学习技术的最短路径距离近似
Shortest path distance approximation using deep learning techniques
摘要：
计算节点之间的最短路径距离是许多图算法和应用程序的核心。传统的精确方法，如广度优先搜索（BFS），不能扩展到当代，快速发展的今天的大规模网络。因此，需要找到近似方法来实现具有显著加速的可扩展图形处理。在本文中，我们利用深度学习技术学习的向量嵌入来近似大型图中的最短路径距离。我们表明，前馈神经网络与嵌入美联储可以近似距离相对较低的失真误差。在Facebook、BlogCatalog、Youtube和Flickr社交网络上对所提出的方法进行了评估。
英文摘要：
Computing shortest path distances between nodes lies at the heart of many graph algorithms and applications. Traditional exact methods such as breadth-first-search (BFS) do not scale up to contemporary, rapidly evolving today's massive networks. Therefore, it is required to find approximation methods to enable scalable graph processing with a significant speedup. In this paper, we utilize vector embeddings learnt by deep learning techniques to approximate the shortest paths distances in large graphs. We show that a feedforward neural network fed with embeddings can approximate distances with relatively low distortion error. The suggested method is evaluated on the Facebook, BlogCatalog, Youtube and Flickr social networks.
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