摘要
On one hand, compared with traditional rela- tional and XML models, graphs have more expressive power and are widely used today. On the other hand, various ap- plications of social computing trigger the pressing need of a new search paradigm. In this article, we argue that big graph search is the one filling this gap. We first introduce the ap- plication of graph search in various scenarios. We then for- malize the graph search problem, and give an analysis of graph search from an evolutionary point of view, followed by the evidences from both the industry and academia. After that, we analyze the difficulties and challenges of big graph search. Finally, we present three classes of techniques to- wards big graph search： query techniques, data techniques and distributed computing techniques.
On one hand, compared with traditional rela- tional and XML models, graphs have more expressive power and are widely used today. On the other hand, various ap- plications of social computing trigger the pressing need of a new search paradigm. In this article, we argue that big graph search is the one filling this gap. We first introduce the ap- plication of graph search in various scenarios. We then for- malize the graph search problem, and give an analysis of graph search from an evolutionary point of view, followed by the evidences from both the industry and academia. After that, we analyze the difficulties and challenges of big graph search. Finally, we present three classes of techniques to- wards big graph search： query techniques, data techniques and distributed computing techniques.
基金
This work was supported in part by 973 program （2014CB340300）, National Natural Science Foundation of China （Grant No. 61322207） and the Fundamental Research Funds for the Central Universi- ties.
