FLEET：基于二分图流的蝴蝶估计
FLEET: Butterfly Estimation from a Bipartite Graph Stream
摘要：
我们认为空间有效的单通估计的蝴蝶，一个基本的二分图图案的数量，从一个大规模的二分图流，其中每个边缘代表在两个不同的分区实体之间的连接。我们提出了一个空间下界的任何流算法，可以准确地估计蝴蝶的数量，以及FLEET，一套算法，准确地估计蝴蝶的数量在图形流。算法返回的估计值在近似误差上有可证明的保证，实验表明在所用空间和近似精度之间有很好的权衡。我们还提出了空间效率的算法，用于估计流中最近的元素的滑动窗口内的蝴蝶的数量。虽然有一个显着的机构的工作计数子图，如三角形在一个unipartite图流，我们的工作似乎是少数几个解决的情况下，二分图流。
英文摘要：
We consider space-efficient single-pass estimation of the number of butterflies, a fundamental bipartite graph motif, from a massive bipartite graph stream where each edge represents a connection between entities in two different partitions. We present a space lower bound for any streaming algorithm that can estimate the number of butterflies accurately, as well as FLEET, a suite of algorithms for accurately estimating the number of butterflies in the graph stream. Estimates returned by the algorithms come with provable guarantees on the approximation error, and experiments show good tradeoffs between the space used and the accuracy of approximation. We also present space-efficient algorithms for estimating the number of butterflies within a sliding window of the most recent elements in the stream. While there is a significant body of work on counting subgraphs such as triangles in a unipartite graph stream, our work seems to be one of the few to tackle the case of bipartite graph streams.
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