QuickPoint: Efficiently Identifying Densest Sub-Graphs in Online Social Networks for Event Stream Dissemination

Abstract

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 ( n 2 ) , 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 ( l o g a n ) ( 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.