The identification of multiple network spreaders is an appropriate solution to spread information, ideas or diseases in many practical applications. For instance, in target marketing, the spreaders are selected from customer groups classified by similar purchase behaviors to advertise the products, and to optimize the allocation of limited resources. The community detection approaches intuitively are used to identify the community structures or social groups in a social/complex network. However, how to determine the number of community K is a difficult issue. Hence, two-phase evolutionary framework (TPEF) is proposed for automatically determining the number of community K and maximizing the modularity of communities. In the preliminary experiment, the LFR benchmark networks are used to test the proposed method, and to analyze the execution time, the community quality and the network spreading effect. The experiment results show that TPEF can perform well and produce the satisfied quality of community structures. The community detection approaches can be used to assist selecting the multiple network spreaders, and to gain the benefit in network spreading when the community structure is obvious. Furthermore, our results suggest that developing an index, a mechanism or a sampling technic is necessary to decide whether the community detection approaches are applied for selecting multiple network spreaders.

The identification of influential spreaders of information via social networks can assist in the acceleration or hindrance of information dissemination, in increased product exposure, and in the detection of contagious disease outbreaks. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, researchers have overlooked node diversity within network structures as a means of measuring spreading ability. The two-step framework described in this paper uses a robust and insensitive measure that combines global diversity and local features (e.g., degree centrality) to identify the most influential social network nodes. Preliminary experiment results indicate that the proposed method performs well and maintains stability in single initial spreader scenarios associated with different social network datasets.

Rain removal from a video is a challenging problem and has been recently investigated extensively. Nevertheless, the problem of rain removal from a single image was rarely studied in the literature, where no temporal information among successive images can be exploited, making the problem very challenging. In this paper, we propose a single-image-based rain removal framework via properly formulating rain removal as an image decomposition problem based on morphological component analysis. Instead of directly applying a conventional image decomposition technique, the proposed method first decomposes an image into the low- and high-frequency (HF) parts using a bilateral filter. The HF part is then decomposed into a "rain component" and a "nonrain component" by performing dictionary learning and sparse coding. As a result, the rain component can be successfully removed from the image while preserving most original image details. Experimental results demonstrate the efficacy of the proposed algorithm.

Rain removal from a video is a challenging problem and has been recently investigated extensively. Nevertheless, the problem of rain removal from a single image has been rarely studied in the literature, where no temporal information among successive images can be exploited, making it more challenging. In this paper, to the best of our knowledge, we are among the first to propose a single-frame-based rain removal framework via properly formulating rain removal as an image decomposition problem based on morphological component analysis (MCA). Instead of directly applying conventional image decomposition technique, we first decompose an image into the low-frequency and high frequency parts using a bilateral filter. The high-frequency part is then decomposed into "rain component" and "non rain component" via performing dictionary learning and sparse coding. As a result, the rain component can be successfully removed from the image while preserving most original image details. Experimental results demonstrate the efficacy of the proposed algorithm.