Showing papers by "Huiping Cao published in 2015"

Efficient keyword search on graphs using MapReduce

Abstract: A solution of a keyword query over graphs is a Group Steiner tree, which is rooted at a node and whose nodes collectively satisfy the query (e.g. node keywords cover all the query keywords), and in which the sum of edge weights satisfies given conditions (e.g., need to be minimum or be the first K minimal among all possible sub-graphs satisfying the query). Most existing techniques for evaluating keyword queries over graphs run on a centralized computer. We propose a new approach, SOverlapping, to evaluate keyword queries over graphs on MapReduce framework by utilizing probabilistic theory to partition graphs. The new approach has shown to be effective and efficient when tested on real graph data sets.

Discovering time-evolving influence from dynamic heterogeneous graphs

Abstract: Influence among objects prevalently exists in graph structured data. However, most existing research efforts detect influence among objects from snapshots of homogeneous graphs. In this paper, we study a new problem of detecting time-evolving influence among objects from dynamic heterogeneous graphs. We propose a probabilistic graphical model, Time-evolving Influence Model (TIM), to capture the temporal dynamics of graphs, in which the time-evolving influence is hidden, and to leverage the information from heterogeneous graphs, with which we can improve the learned knowledge. To learn the graphical model, we design both non-parallel and parallel Gibbs sampling algorithms. We conduct extensive experiments on both synthetic and real data sets to show the effectiveness of the proposed model and the efficiency of the learning algorithms.