学术报告

报告摘要

With the rapid development of social media, online communities, mobile communications, huge volumes of digital data are accumulated with data objects often involving complex relationships. Consequently, the accumulated data are usually modelled as graphs, where objects are represented by vertices and relationships are represented by edges. An availability of rich graph data not only brings great opportunities for realizing big values of data to serve key applications but also brings great challenges in computation. In this talk, I will focus on an important research topic, called cohesive subgraph search over large graphs. In particular, I will discuss two sub-topics, namely community search and densest subgraph discovery. The first one aims to efficiently search the community of a query vertex over large graphs, while the second one computes the subgraph whose density is the highest. Both efficient algorithms and experimental results will be discussed. In addition, a system prototype will be presented.