论文标题
大型图形的分布式内存中心网络嵌入
Distributed-Memory Vertex-Centric Network Embedding for Large-Scale Graphs
论文作者
论文摘要
网络嵌入是基于图形数据的许多不同计算中的重要步骤。但是，现有方法仅限于中小型图，边缘不到一百万。在实践中，网络或社交网络图是较大的数量级，因此对于非常大的图表而言，大多数当前方法都不切实际。为了解决此问题，我们引入了基于Apache Spark和GraphX的新分布式记忆并行网络嵌入方法。我们演示了我们方法的可扩展性及其生成有意义的嵌入以进行顶点分类并在现实世界和合成图上链接预测的能力。
Network embedding is an important step in many different computations based on graph data. However, existing approaches are limited to small or middle size graphs with fewer than a million edges. In practice, web or social network graphs are orders of magnitude larger, thus making most current methods impractical for very large graphs. To address this problem, we introduce a new distributed-memory parallel network embedding method based on Apache Spark and GraphX. We demonstrate the scalability of our method as well as its ability to generate meaningful embeddings for vertex classification and link prediction on both real-world and synthetic graphs.
