流媒体中的Denmark子图与MapReduce
Densest Subgraph in Streaming and MapReduce
摘要：
寻找图的局部稠密成分的问题是数据分析中的一个重要问题，具有广泛的应用，从社区挖掘到垃圾邮件检测和生物网络模块的发现。在本文中，我们提出了新的算法，发现在流模型中的denominator子图。对于任何n>0，我们的算法使O（（log n）/log（1+ n））通过输入，并找到一个子图，其密度保证在一个因素2（1+ n）的最佳。我们的算法也很容易并行化，我们通过在MapReduce模型中实现它们来说明这一点。此外，我们进行了大量的真实世界的图显示我们的算法在实践中的性能和可扩展性进行了广泛的实验评估。
英文摘要：
The problem of finding locally dense components of a graph is an important primitive in data analysis, with wide-ranging applications from community mining to spam detection and the discovery of biological network modules. In this paper we present new algorithms for finding the densest subgraph in the streaming model. For any epsilon>0, our algorithms make O((log n)/log (1+epsilon)) passes over the input and find a subgraph whose density is guaranteed to be within a factor 2(1+epsilon) of the optimum. Our algorithms are also easily parallelizable and we illustrate this by realizing them in the MapReduce model. In addition we perform extensive experimental evaluation on massive real-world graphs showing the performance and scalability of our algorithms in practice.
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