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Learning now occurs in various manners in social networks, utilizing practice communities and learning networks. In this context, students are interested in exploring learning activities of other students without having to read through large quantities of textual content. Students tend to be interested in finding information concerning their majors, contents of their subjects and their co-learners...
Influence maximization aims to find a set of highly influential nodes in a social network to maximize the spread of influence. The most difficult part of the problem is to estimate the influence spread of any seed set, which has been proved to be #P-hard. There is no efficient method to estimate the influence spread of any seed set till now. Thus, the most common way to obtain the approximate influence...
Social media have become an important source of data and can provide near-instantaneous information which can be analysed to generate predictive models and to support decision making. Much work has been done in short message analysis such as trend analysis, short message classification, etc. However, to generate an accurate and concise conclusion/assertion from all the relevant information remains...
An information cascade occurs when a person observes the actions of others and then engages in the same acts. Cascades may break out if a large population of nodes in the network get affected. The outbreaks of cascades will often bring influential events, which leads to an open research problem: how to accurately predict the cascading outbreaks in social networks? Although there have been some existing...
Complex social network analysis methods have been applied extensively in various domains including online social media, biological complex networks, etc. Complex social networks are facing the challenge of information overload. The demands for efficient complex network analysis methods have been rising in recent years, particularly the extensive use of online social applications, such as Flickr, Facebook...
It is well-known that recommendation system which is widely used in many e-commerce platforms to recommend items to the right users suffers from data sparsity, imbalanced rating and cold start problems. Matrix factorization is a good way to deal with the sparsity and imbalance problems, which is however unable to make prediction for new users due to the lack of auxiliary information. With the advent...
Pluralistic ignorance (PI) is a common phenomenon, observed in many social settings. It occurs when the majority of a group become non-believer conformist, but mistakenly perceive others to be true conformist. PI takes many forms and leads to a wide variety of social problems, from binge drinking to repressive political regimes and ideologies. Although discussed extensively in the literature, the...
In new media era, users post messages to record their daily lives and express their opinions via social media platforms, such as microblog. Recently, it is an attractive topic to tag users from the users generation contents. Tags for a microblog user, as the description for his/her interests, concerns or occupational characteristics, are playing an important role in user indexing, personalized recommendation,...
A microblog recommendation method based on tag correlation and user social relation is proposed via analyzing microblog features and the deficiencies of existing microblog recommendation algorithm. Specifically, a tag retrieval strategy is established to add tags for unlabeled users and users with few tags, and the user-tag matrix is then built and user-tag weights are then obtained. In order to solve...
It is now a fact as well as a trend that people are increasingly relying on online social networks to work, study, and share with others. Thus, it is unavoidable for us to be influenced by others through online social networking. Studying social influence and information diffusion in online social networks can be remarkably useful in various real-life applications, notably influencer marketing and...
Leveraging expert attributes and their attribute-associated features, we propose an expert disambiguation method based on experts' attributed graph clustering model. In the method, firstly, the attributes and their co-occurrences are identified and extracted. Secondly, based on graph theory, the augmented expert attribute nodes are established and their correlations are connected to form a network...
This work addresses short-term traffic flow prediction by proposing a big-data-based framework. The proposed framework uses data fusion to deal with heterogeneous data generated from various sources. The data are categorized into two types: streams of data and event-based data. In this work, Deep Belief Networks (DBNs) are used to independently predict traffic flow using streams of data, i.e., historical...
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