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Computing Large Connected Components Using Map Reduce in Logarithmic Rounds

机译:使用地图计算大连接组件在对数轮中减少

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Graph structures are often used for representingdata object and link between them in large datasets. Knowledge extraction from these data relies on finding the connected components within these graphs. Given a large graph G = (V, E), where V is the set of vertices and E is the set of edges, the problem is to find the connected components efficiently. The problem offinding the connected components is labeling each vertex with its graph component number. Recent works have been done to address this problem in MapReduce, but there always exists atrade-off between number of rounds and the communications perround. Let d be the diameter of the graph and n be the numberof nodes in the largest component, all the prior implementationsof this algorithm in MapReduce take linear, O(d), number of mapreduce rounds and quadratic, O(n|V| +|E|), communications perround. The efficient algorithm proposed here finishes it in 2(log d) round and take (|V| + |E|) number of communications perround (where d = diameter of graph with V vertices and E edges, n = Number of nodes in largest component).
机译:图形结构通常用于表示在大型数据集中的代表数据对象和链接。来自这些数据的知识提取依赖于在这些图中找到连接的组件。给定大图G =(v,e),其中V是顶点和e是一组边缘,问题是有效地找到连接的组件。脱离连接组件的问题正在将每个顶点标记为其图形组件编号。最近的作品已经完成了在MapReduce中解决这个问题,但随着轮次数量和通信频,始终存在苛刻。让D是图表的直径,n是最大组件中的Node,在MapReduce中的所有先前实现都采取了线性,o(d),MapReduce圆数和二次,O(n | v | + | e |),通信围机。这里提出的有效算法在2(log d)圆形中完成它,并采取(| v | + | e |)通信次数(其中d =与V顶点和e边的图表的直径,n =最大的节点数成分)。

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