How can we estimate local triangle counts accurately in a graph stream without storing the whole graph? How to handle duplicated edges in local triangle counting for graph stream? Local triangle counting, which computes the number of triangles attached to each node in a graph, is a very important problem with wide applications in social network analysis, anomaly detection, web mining, and the like. In this article, we propose algorithms for local triangle counting in a graph stream based on edge sampling: M ascot for a simple graph, and M ulti BM ascot and M ulti WM ascot for a multigraph. To develop M ascot , we first present two naive local triangle counting algorithms in a graph stream, called M ascot -C and M ascot -A. M ascot -C is based on constant edge sampling, and M ascot -A improves its accuracy by utilizing more memory spaces. M ascot achieves both accuracy and memory-efficiency of the two algorithms by unconditional triangle counting for a new edge, regardless of whether it is sampled or not. Extending the idea to a multigraph, we develop two algorithms M ulti BM ascot and M ulti WM ascot . M ulti BM ascot enables local triangle counting on the corresponding simple graph of a streamed multigraph without explicit graph conversion; M ulti WM ascot considers repeated occurrences of an edge as its weight and counts each triangle as the product of its three edge weights. In contrast to the existing algorithm that requires prior knowledge on the target graph and appropriately set parameters, our proposed algorithms require only one parameter of edge sampling probability. Through extensive experiments, we show that for the same number of edges sampled, M ascot provides the best accuracy compared to the existing algorithm as well as M ascot -C and M ascot -A. We also demonstrate that M ulti BM ascot on a multigraph is comparable to M ascot -C on the counterpart simple graph, and M ulti WM ascot becomes more accurate for higher degree nodes. Thanks to M ascot , we also discover interesting anomalous patterns in real graphs, including core-peripheries in the web, a bimodal call pattern in a phone call history, and intensive collaboration in DBLP.

Strategic public health communication efforts in public health preparedness and during emergencies should take into account potential communication inequalities and develop campaigns that reach across different social groups.

Health knowledge is necessary for personal healthcare management. The public obtains such health knowledge not only from healthcare providers, but also through daily media exposure. Health information became highly universalized amid a wave of health news, pharmaceutical advertisements, and health websites coupled with the recent emergence of user-generated Internet content (i.e. blogs) based on diverse information and communication platforms. Despite the abundance of health information, however, huge disparities exist between individuals in their levels of health knowledge, their interest in health information, and their health information-seeking behaviors (Viswanath, 2005). Therefore, numerous advanced countries spotlight health informationseeking behaviors (HISBs) as a key element of health communication.Free access, active search, and accurate understanding and use of health information heavily influence a healthy lifestyle, early diagnosis of disease, disease control, participation in medical decision-making, understanding of therapeutic processes, and the treatment of ultimately terminal patients or post-treatment cancer patients (Van der Molem, 1999;Viswanath et al., 2012). Formerly, doctors acted as the sole providers of health information, but technological advances in communication and information dissemination created an environment that offers diverse sources of information related to health management (Fallowfield et al., 1995 AbstractHealth information-seeking behavior (HISB) is active need-fulfillment behavior whereby health information is obtained from diverse sources, such as the media, and has emerged as an important issue within the transforming medical environment and the rise of medical consumers. However, little is known about the factors that affect HISB and its associations, and the health outcome of HISB. The aim of this study was to examine individual and social contextual factors associated with HISB and to systematically review their effects on health status among posttreatment cancer patients. Individual determinants of HISB included demographic factors, psychosocial factors, perceived efficacy and norms, and health beliefs. Contextual determinants of HISB encompassed community characteristics, neighborhood social capital, and media advocacy. Improving through factors on these two levels, HISB raised individuals' self-care management skills and medical treatment compliance, and enhanced shared decision-making and medical treatment satisfaction. Moreover, because HISB can differ according to individuals' social contextual conditions, it can give rise to communication inequalities. Because these can ultimately lead to health disparities between groups, social interest in HISB and balanced HISB promotion strategies are necessary.

It is well known that the visual cortex efficiently processes high-dimensional spatial information by using a hierarchical structure. Recently, computational models that were inspired by the spatial hierarchy of the visual cortex have shown remarkable performance in image recognition. Up to now, however, most biological and computational modeling studies have mainly focused on the spatial domain and do not discuss temporal domain processing of the visual cortex. Several studies on the visual cortex and other brain areas associated with motor control support that the brain also uses its hierarchical structure as a processing mechanism for temporal information. Based on the success of previous computational models using spatial hierarchy and temporal hierarchy observed in the brain, the current report introduces a novel neural network model for the recognition of dynamic visual image patterns based solely on the learning of exemplars. This model is characterized by the application of both spatial and temporal constraints on local neural activities, resulting in the self-organization of a spatio-temporal hierarchy necessary for the recognition of complex dynamic visual image patterns. The evaluation with the Weizmann dataset in recognition of a set of prototypical human movement patterns showed that the proposed model is significantly robust in recognizing dynamically occluded visual patterns compared to other baseline models. Furthermore, an evaluation test for the recognition of concatenated sequences of those prototypical movement patterns indicated that the model is endowed with a remarkable capability for the contextual recognition of long-range dynamic visual image patterns.