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Community detection is one of the hot topics in the complex networks. It aims to find subgraphs that are internally dense but externally sparsely connected. In this paper, a new method is proposed to identify the most influential nodes which are considered as cores of communities and achieve the initial communities. Then, by an expansion strategy, unassigned nodes are added to initial communities to expand communities. Finally, merging overlapping communities to get the final community structure. To evaluate the performance of the proposed node influence method (LGI), the susceptible–infected–removed (SIR) diffusion model are used. Testing with the synthetic networks and real-world networks, LGI can identify best nodes with high influence and is better than other centrality methods. Finally, experiments show that our proposed community detection algorithm based on influential nodes (LGIEM) is able to detect communities efficiently, and achieves better performance compared to other recent methods.

Nowadays, the amount of user-generated contents (UGCs) or texts has surged exponentially. Therefore, recognizing emotions from these texts can bring about lots of advantages. In this paper, we have proposed a novel model named Deep Rolling to predict emotion for target participant in a multi-participant communication context. First, the proposed method converts a text collection into a set of n -dimension vectors for emotion representation and re-organizes texts into a sequence in time order. Then, Deep Rolling can predict the emotion of target participant corresponding to a future time point . Second, apart from simply taking in texts posted by target participant via LSTM , the proposed method has also incorporated texts posted by other participants at every time step by CNN . In this way, Deep Rolling can predict target participant’s emotion by processing emotions from both the target and all the other participants in an ensemble way. Finally, data factorization has also been introduced into Deep Rolling to enhance the overall prediction efficiency. According to experimental results, compared with the state-of-art methods, our proposed model has achieved the best prediction precision on different target participants. At the same time, Deep Rolling has also maintained the prediction efficiency at an acceptable level.

In this paper, we will propose a novel approach based on graph analysis which will use community structure detection algorithm to detect topics in the keywords graph of micro-blogging data. Furthermore, considering the specificity of the Sina microblogging, we propose novel keywords filtering model and graph generation algorithm to meet the dual requirements of topic detection and community detection. We validate our approach on a big natural disaster dataset from Sina micro-blog, in which about 103 micro-blogging posts with about 104 distinct feature tags. The experimental results definitely revealed the relationship between the keywords and the natural disaster topics. Our methodology is a scalable method which can adapt to the changes in the amount of data. Especially, we can get abundant information about natural disasters in the topic detection and help the government guide the rescue of disasters.

With the rapid growth of social network, the cost of computation is increasing. Many existing algorithms are not suitable for the large-scale data. Apache Spark is an open-source cluster computing framework that empowers us to solve the problem of community detection in a cluster of computer. In this paper, we propose a novel label propagation algorithm on Spark, called PSPLPA (Probability and similarity based Parallel label propagation algorithm). PSPLPA employs a new label updating strategy using probability in the label propagation procedure during each iteration. First, weight calculation, which is based on k-shell, is integrated into the label initialization process. Second, parallel propagation steps are comprehensively proposed to utilize label probability efficiently. Third, randomness in label updating is significantly reduced via automatic label selection and similarity computation. Experiments conducted on artificial and real social networks demonstrate that the proposed algorithm exhibits high scalability and high accuracy.

With the development of mobile Internet technology, location-based services (LBS) has been widely used in social networks, such as WeChat. A user of WeChat can discover people nearby with the help of LBS. However, users need to send their current locations to get the service, which may lead to the leakage of their location privacy. An effective way to protect the user's location privacy is using an imprecise location, such as a region to replace his real location. When processing continuous queries, the user need to transmit the region to the server continuously and an attacker is able to infer the real location from the overlapping regions. In this paper, we use circle as the query region and describe how to form a circle region. Then we study the overlapping circle attack and design a method to avoid this attack by changing the confidence level to a fixed value. We also propose an algorithm E PPINQ for the server to process a k NN query while protecting user's location against the attack. Experimental results show that our algorithm is more effective and efficient than the existing algorithm.

Recommender systems play an important role in our daily life and are becoming popular tools for users to find what they are really interested in. Matrix factorization methods, which are popular recommendation methods, have gained high attention these years. With the rapid growth of the Internet, lots of information has been created, like social network information, tags and so on. Along with these, a few matrix factorization approaches have been proposed which incorporate the personalized information of users or items. However, except for ratings, most of the matrix factorization models have utilized only one kind of information to understand users’ interests. Considering the sparsity of information, in this paper, we try to investigate the combination of different information, like tags and genres, to reveal users’ interests accurately. With regard to the generalization of genres, a constraint is added when genres are utilized to find users’ similar “soulmates”. In addition, item regularizer is also considered based on latent semantic indexing (LSI) method with the item tags. Our experiments are conducted on two real datasets: Movielens dataset and Douban dataset. The experimental results demonstrate that the combination of tags and genres is really helpful to reveal users’ interests.

Community detection in social networks is a fundamental task of complex network analysis. Community is usually regarded as a functional unit. Networks in real world more or less have overlapping community structure while traditional community detection algorithms assume that one vertex can only belong to one community. This paper proposes an efficient overlapping community detection algorithm named LED (Loop Edges Delete). LED algorithm is based on Structural Clustering, which converts structural similarity between vertices to weights of network. The evaluations of the LED algorithm are conducted both from classical networks from literature and C-DBLP, which is a huge and real-life co-author social network in China. The results show that LED is superior to other methods in accuracy, efficiency, comparing with FastModurity and GN algorithm.

As a research branch of data mining, clustering, as an unsupervised learning scheme, focuses on assigning objects in the dataset into several groups, called clusters, without any prior knowledge. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is one of the most widely used clustering algorithms for spatial datasets, which can detect any shapes of clusters and can automatically identify noise points. However, there are several troublesome limitations of DBSCAN: (1) the performance of the algorithm depends on two specified parameters, e and MinPts in which e represents the maximum radius of a neighborhood from the observing point and MinPts means the minimum number of data points contained in such a neighborhood. (2) The time consumption for searching the nearest neighbors of each object is intolerable in the cluster expansion. (3) Selecting different starting points results in quite different consequences. (4) DBSCAN is unable to identify adjacent clusters of various densities. In addition to these restrictions about DBSCAN mentioned above, the identification of border points is often ignored. In our paper, we successfully solve the above problems. Firstly, we improve the traditional locality sensitive hashing method to implement fast query of nearest neighbors. Secondly, several definitions are redefined on the basis of the influence space of each object, which takes the nearest neighbors and the reverse nearest neighbors into account. The influence space is proved to be sensitive to local density changes to successfully reduce the amount of parameters and identify adjacent clusters of different densities. Moreover, this new relationship based on the influence space makes the insensitivity to the ordering of inputting points possible. Finally, a new concept-core density reachable based on the influence space is put forward which aims to distinguish between border objects and noisy objects. Several experiments are performed which demonstrate that the performance of our proposed algorithm is better than the traditional DBSCAN algorithm and the improved algorithm IS-DBSCAN.

In classical Laplacian image sharpening, all pixels are processed one by one, which leads to large amount of computation. Traditional Laplacian sharpening processed on CPU is considerably time-consuming especially for those large pictures. In this paper, we propose a parallel implementation of Laplacian sharpening based on Compute Unified Device Architecture (CUDA), which is a computing platform of Graphic Processing Units (GPU), and analyze the impact of picture size on performance and the relationship between the processing time of between data transfer time and parallel computing time. Further, according to different features of different memory, an improved scheme of our method is developed, which exploits shared memory in GPU instead of global memory and further increases the efficiency. Experimental results prove that two novel algorithms outperform traditional consequentially method based on OpenCV in the aspect of computing speed.

The conventional border patrol systems suffer from intensive human involvement. Recently, unmanned border patrol systems employ high-tech devices, such as unmanned aerial vehicles, unattended ground sensors, and surveillance towers equipped with camera sensors. However, any single technique encounters inextricable problems, such as high false alarm rate and line-of-sight-constraints. There lacks a coherent system that coordinates various technologies to improve the system accuracy. In this paper, the concept of BorderSense, a hybrid wireless sensor network architecture for border patrol systems, is introduced. BorderSense utilizes the most advanced sensor network technologies, including the wireless multimedia sensor networks and the wireless underground sensor networks. The framework to deploy and operate BorderSense is developed. Based on the framework, research challenges and open research issues are discussed.

Underground pipelines constitute one of the most important ways to transport large amounts of fluid (e.g. oil and water) through long distances. However, existing leakage detection techniques do not work well in monitoring the underground pipelines due to the harsh underground environmental conditions. In this paper, a new solution, the magnetic induction (MI)-based wireless sensor network for underground pipeline monitoring (MISE-PIPE), is introduced to provide low-cost and real-time leakage detection and localization for underground pipelines. MISE-PIPE detects and localizes leakage by jointly utilizing the measurements of different types of sensors that are located both inside and around the underground pipelines. By adopting an MI waveguide technique, the measurements of different types of the sensors throughout the pipeline network can be reported to the administration center in real-time. The system architecture and operational framework of MISE-PIPE is first developed. Based on the operational framework, research challenges and open research issues are then discussed.

As serious natural disasters, sandstorms have caused massive damages to the natural environment, national economy, and human health in the Middle East, Northern Africa, and Northern China. To avoid such damages, wireless sensor networks (WSNs) can be deployed in the regions where sandstorms generally originate so that sensor nodes can collaboratively monitor the origin and development of sandstorms. Despite the potential advantages, the deployment of WSNs in the vicinity of sandstorms faces many unique challenges, such as the temporally buried sensors and increased path loss during sandstorms. Consequently, the WSNs may experience frequent disconnections during the sandstorms. In this paper, the connectivity issue of WSNs for sandstorm monitoring is studied. First, four types of channels a sensor can utilize during sandstorms are analyzed, which include air-to-air channel, air-to-sand channel, sand-to-air channel, and sand-to-sand channel. Based on these analytical results, the percolation-based connectivity analysis is performed. It is shown that if the sensors are buried in shallow depth, allowing sensor to use multiple types of channels improves network connectivity. Accordingly, much smaller sensor density is required compared to the case, where only terrestrial air channels are used. Through this connectivity analysis, a WSN architecture can be established for efficient and effective sandstorm monitoring.