Querying Dynamics in Evolving Graphs and Networks

在社交网络分析、欺诈检测、网络质量诊断等许多应用中，大量的复杂数据被建模为图或网络。图表和网络使我们能够分析过去无法全面理解的复杂行为，例如社交网络中的影响传播和群体协作。这样的动态是图表和网络数据的一大亮点。此外，由于建模为图和网络的数据的规模和复杂性，变化和演变是不可避免的。分析和利用不断演变的图形和网络中的动力学，一方面为我们提供了征服大数据的前所未有的力量和工具，另一方面也带来了重大的技术挑战。这项拟议的研究是为了抓住机遇，应对技术挑战。

In many applications, such as social network analysis, fraud detection, and network quality diagnosis, a large amount of complex data are modeled as graphs or networks. Graphs and networks enable us to analyze sophisticated behavior that cannot be understood comprehensively in the past, such as influence propagation and group collaboration in a social network. Such dynamics are a beauty in graph and network data. Moreover, due to the scale and the complexity of data modeled as graphs and networks, changes and evolutions are inevitable. Analyzing and making good use of dynamics in evolving graphs and networks, on the one hand, provide us unprecedented power and tools to conquer big data, and, on the other hand, post grand technical challenges. This proposed research is to embrace the opportunities and address the technical challenges.