Survey of Probabilistic Models Combining Content and Link for Network Structure Detection
With the development of social media,many online networks generate a lot of contents. Discovering their latent structures can help us understand their functions,analyze and predict them deeply. Community and topic are two important bases for structure exploring,which respectively makes use of network's links and contents to model. But the results from community detection are unintelligibly due to the sparse links,and the ones from topic identification are inaccuracy because of the irrelevant contents. In order to resolve these problems,the probabilistic models combining content and link become prevalent,which are classified as topic identification models,topic community detection models and community-topic detection models. The article analyzes each classical model' s designing background,keystone and solving method,and explores their existing problems through the qualitative comparisons and experiment analysis. In the end the research progress about the combining models is predicted.