论文标题
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
论文作者
论文摘要
计算节点之间的最短路径距离是许多图算法和应用的核心。传统的精确方法，例如广度优先搜索（BFS），并不能扩展到当代，快速发展的当今大型网络。因此，需要找到近似方法以启用具有显着加速的可伸缩图处理。在本文中，我们利用深度学习技术学到的向量嵌入来近似大图中的最短路径距离。我们表明，带有嵌入的馈电神经网络可以近似距离，而失真误差相对较低。建议的方法在Facebook，BlogCatalog，YouTube和Flickr社交网络上进行评估。
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.
