The power of online social networks to propagate information within communities and from one community to the next is undeniable. Both network structure and information propagation affect each other; they restrict and cooperate with each other. However, they can also dynamically reshape the network topology of user’s social relationship in this process. The above process ultimately forms a feedback loop: the network structure affects how information spreads, while information propagation reshapes network topologies, so both evolve in concert over time. Using information propagation trees (IPT) of posts from the Sina Weibo microblogging site, we conducted a null model-based analysis to determine the influence of community structures on information propagation. We first generated randomized copies of the IPTs and then mined community structures from the originals and copies for comparison. An in-depth examination of the results in terms of improved significant profile, the length of information propagation path, and the relevance of the nodes in the propagation path indirectly reveals the inhibitory effect of community structures on information propagation.
With the rapid development of mobile terminal devices, mobile user activities can be carried out anytime and anywhere through various mobile terminals. The current research on mobile communication network is mainly focused on extracting useful and interesting information for mobile user from massive and disordered information. However, the sparsity of scoring data matrix results in low quality of recommendation algorithm. In order to overcome this drawback, the traditional collaborative filtering algorithm is improved. First, the user-interest matrix and item-feature matrix were obtained by analyzing mobile user behavior and item attributes. Fuzzy trust based model is utilized for collaborative filtering analysis for mobile user preferences. Then, the similarity between different mobile users was calculated by weighted calculation. With this method, mobile user preference can be predicted effectively, making it possible to recommend rational resource and waste less time in extracting resources out of the massive information. Experimental results show that the proposed algorithm reduces the mean absolute error (MAE) and the impact of sparse scoring matrix data compared with the traditional collaborative filtering algorithm, and improves the recommendation effect to a certain extent.
With the increasingly popularity of Q & A Community, it has become an important means for people to retrieve question from question library to find the answer. Similarity calculation is the core issue in Q & A community, and the appropriate calculation method is the key factor that affects the quality of question retrieval. This paper proposes a retrieval method based on PLSA model. Firstly, we modelled the question library, and got the probability distribution of "question document-latent semantic-word". Secondly, we calculated the semantic similarity between questions and classify them. Finally, based on user retrieval content, we calculated the similarity between question documents and query, then the query results will be returned to the user in descending order according to the value. Compared with other similarity calculation methods that use VSM, HNS and SD, the experimental results show that this proposed method has a high precision rate.
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