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We propose graph-pattern association rules (GPARs) for social media marketing. Extending association rules for itemsets, GPARs help us discover regularities between entities in social graphs, and identify potential customers by exploring social influence. We study the problem of discovering topk diversified GPARs. While this problem is NP-hard, we develop a parallel algorithm with accuracy bound. We also study the problem of identifying potential customers with GPARs. While it is also NP-hard, we provide a parallel scalable algorithm that guarantees a polynomial speedup over sequential algorithms with the increase of processors. Using real-life and synthetic graphs, we experimentally verify the scalability and effectiveness of the algorithms.

In order to complete the task of the woodland census in Chenzhou, China, this paper carries out a remote sensing survey on the terrain of this area to produce a data set, and used deep learning methods to label the woodland. There are two main improvements in our paper: Firstly, this paper comparatively analyzes the semantic segmentation effects of different deep learning models on remote sensing image datasets in Chenzhou. Secondly, this paper proposed a dense fully convolutional network (DFCN) which combines dense network with FCN model and achieves good semantic segmentation effect. DFCN method is used to label the woodland in Gaofen-2 (GF-2) remote sensing images in Chenzhou. Under the same experimental conditions, the labeling results are compared with the original FCN, SegNet, dilated convolutional network, and so on. In these experiments, the global pixel accuracy of DFCN is 91.5%, and the prediction accuracy of the "woodland" class is 93%, both of them perform better than that of the other methods. In other indicators, our method also has better performance. Using the method of this paper, we have completed the land feature labeling of Chenzhou area and provided it to customers.

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In social media, trust and distrust among users are important factors in helping users make decisions, dissect information, and receive recommendations. However, the sparsity and imbalance of social relations bring great difficulties and challenges in predicting trust and distrust. Meanwhile, there are numerous inducing factors to determine trust and distrust relations. The relationship among inducing factors may be dependency, independence, and conflicting. Dempster-Shafer theory and neural network are effective and efficient strategies to deal with these difficulties and challenges. In this paper, we study trust and distrust prediction based on the combination of Dempster-Shafer theory and neural network. We firstly analyze the inducing factors about trust and distrust, namely, homophily, status theory, and emotion tendency. Then, we quantify inducing factors of trust and distrust, take these features as evidences, and construct evidence prototype as input nodes of multilayer neural network. Finally, we propose a framework of predicting trust and distrust which uses multilayer neural network to model the implementing process of Dempster-Shafer theory in different hidden layers, aiming to overcome the disadvantage of Dempster-Shafer theory without optimization method. Experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework.