In social networks, most information is stored, displayed and propagated in the form of text which is characterized by latent topics. With the discovery of latent topics, we can make more accurate prediction, inference and therefore, better applications, in social networks. Considering the dynamics and noise in social networks, we analyze data evolution from different perspectives including both common and discriminative topics. We adopt a basic model to factorize data matrix into components with an order representing latent topic and apply non-negative matrix factorization to two datasets as contrast to each other simultaneously. Time is taken into consideration as one order of the data matrix. Our basic model converts the decomposition problem into an optimization problem solved by block coordinate gradient descent, based on which the penalized model separates common part and discriminative part by adding penalty items. Next, a Bayesian generative model is established to avoid parameter tuning and overfitting, with the model parameters estimated via Gibbs sampling. Finally, we apply the models to both synthesized data and contrastive corpora of papers accepted by different conferences, as well as provide analysis of topic variation along with time.

Abstract In this paper, we focus on maximizing the influence of online social networks (OSNs). Particularly, we try to answer how to select proper information initiators such that information can propagate as widely as possible. We stress our attention on the susceptible‐infected model, a type of epidemic models, to describe the process of information diffusion. In general, OSNs can be classified into two categories, Facebook‐like OSNs and Twitter‐like OSNs. The former ones require bidirectional connections, while the latter do not, so we use the undirected unweighted graph and directed unweighted graph to describe them, respectively. We also pay additional attention to the nonidentity of the link probability on information transmission and build the weight graph, which can also cover both the two types of OSNs. In order to determine values of weight graph's weights, we introduce a learning method to obtain useful factors from raw data for assessing the true link probability on information transmission. Based on spectral analysis within the three graphs, our investigations on the information diffusion show that the spectral radius of the graph adjacency matrix can reflect the capability of information propagation, according to which we could determine effective initiators. We conduct our simulations on real OSNs. Experimental results show that our approach could effectively discover the initiators that spread information widely. Copyright © 2016 John Wiley & Sons, Ltd.

Online Social Network becomes the most prevalent platform for exchanging information between users, maintaining friendships online. As is well-known to us, however, some friendships even those intimate ones might vanish. Therefore, precisely modeling and predicting state of each online relationship is worthwhile in many respects. For social communication services such modeling permits new and novel online services. In addition, constructing this model might enlighten us in exploiting information spreading pattern in online social network. In this paper, we propose a model in determining a probability distribution which describes the ‘surviving time’ of each friendships by applying one commonly used method in sociology, survival analysis. We discuss a series of social explanatory variables that highly affect this probability distribution. Moreover, methods in the moving average process are devoted to determining the appropriate parameter in survival model. Furthermore, to avoid the high computational complexity in kernel learning we impose sparsity in our model. Finally, with the experiments on real data, the proposed survival model is proven to be of high accuracy, and thus of great potential for further applications.

Recently, security of cognitive radio (CR) is becoming a severe issue. There is one kind of threat, which we call greedy spectrum occupancy threat (GSOT) in this paper, has long been ignored in previous work. In GSOT, a secondary user may selfishly occupy the spectrum for a long time, which makes other users suffer additional waiting time in queue to access the spectrum and leads to congestion or breakdown. In this paper, a queueing model is established to describe the system with greedy secondary user (GSU). Based on this model, the impacts of GSU on the system are evaluated. Numerical results indicate that the steady-state performance of the system is influenced not only by average occupancy time, but also by the number of users as well as number of channels. Since a sudden change in average occupancy time of GSU will produce dramatic performance degradation, the greedy second user prefers to increase its occupancy time in a gradual manner in case it is detected easily. Once it reaches its targeted occupancy time, the system will be in steady state, and the performance will be degraded. In order to detect such a cunning behavior as quickly as possible, we propose a wavelet based detection approach. Simulation results are presented to demonstrate the effectiveness and quickness of the proposed approach.