关键词: 广义社区发现, 大规模内容网络, 随机块模型, 抽样

Abstract: Without any prior knowledge about networks, the PPSB-DC (popularity and productivity stochastic block model and discriminative content model) models the generative process by contents and links, which makes it be able to detect general communities and identify link patterns between any two communities. However, the algorithm for this probabilistic model costs much time and is sensible to the initial parameters of link patterns. These disadvantages limit the application of the algorithm. In order to improve the parameter estimation algorithm, this paper proposes an efficient algorithm for general community detection in content networks EPPSBDC (efficient PPSB-DC). EPPSBDC improves the speed by sampling and parallel strategies, and decreases the sensibility for the initial parameters by introducing a prior of link pattern. Comparisons of similar algorithms in content networks demonstrate the validity of EPPSBDC.

Key words: general community detection, massive content networks, stochastic block model, sampling