Efficient event stream dissemination is a challenging problem in large-scale Online Social Network (OSN) systems due to the costly inter-server communications caused by the per-user view data storage. To solve the problem, previous schemes mainly explore the structures of social graphs to reduce the inter-server traffic. Based on the observation of high cluster coefficients in OSNs, a state-of-the-art social piggyback scheme can save redundant messages by exploiting an intrinsic hub-structure in an OSN graph for message piggybacking. Essentially, finding the best hub-structure for piggybacking is equivalent to finding a variation of the densest sub-graph. The existing scheme computes the best hub-structure by iteratively removing the node with the minimum weighted degree. Such a scheme incurs a worst computation cost of $O(n^2)$O(n2), making it not scalable to large-scale OSN graphs. Using alternative hub-structure instead of the best hub-structure can speed up the piggyback assignment. However, they greatly sacrifice the communication efficiency of the assignment schedule. Different from the existing designs, in this work, we propose a QuickPoint algorithm, which removes a fraction of nodes in each iteration in finding the best hub-structure. We mathematically prove that QuickPoint converges in $O(log_an) (a>1)$O(logan)(a>1) iterations in finding the best hub-structure for efficient piggyback. We implement QuickPoint in parallel atop Pregel, a vertex-centric distributed graph processing platform. Comprehensive experiments using large-scale data from Twitter and Flickr show that our scheme is 38.8× more efficient compared to existing schemes.

中文翻译：

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QuickPoint：有效识别在线社交网络中最密集的子图以进行事件流传播

有效的事件流传播是大规模的具有挑战性的问题 在线社交网络(OSN) 系统由于每个用户的视图数据存储导致昂贵的服务器间通信。为了解决这个问题，以前的方案主要探索社交图的结构来减少服务器间的流量。基于对 OSN 中高聚类系数的观察，最先进的社交捎带方案可以通过利用 OSN 图中的内在集线器结构进行消息捎带来节省冗余消息。从本质上讲，为搭载找到最佳的集线器结构等同于找到最密集子图的变体。现有方案通过迭代删除具有最小加权度的节点来计算最佳集线器结构。这种方案招致最坏的计算成本$O(n^2)$哦(n2)，使其无法扩展到大规模 OSN 图。使用替代集线器结构而不是最佳集线器结构可以加快搭载分配。然而，它们极大地牺牲了分配时间表的通信效率。与现有设计不同，在这项工作中，我们提出了一种 QuickPoint 算法，该算法在每次迭代中删除一小部分节点以寻找最佳集线器结构。我们在数学上证明 QuickPoint 收敛于$O(log_an) (a>1)$哦(升○G一种n)(一种>1)寻找最佳集线器结构以实现高效搭载的迭代。我们在 Pregel（一个以顶点为中心的分布式图形处理平台）之上并行实施 QuickPoint。使用来自 Twitter 和 Flickr 的大规模数据的综合实验表明，与现有方案相比，我们的方案效率提高了 38.8 倍。