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Social data from online social networks is expanding rapidly as the number of users and articles posted increases, making public opinion analysis a greater challenge. Real-time topic detection is a key part of public opinion analysis. The complex data processing involved in traditional clustering and text categorization can lead to time delays in topic detection. In this paper we construct similar...
Recently, some researchers attempt to find a relationship between the evolution of rare events and temporal-spatial patterns of social media activities. Their studies verify that the relationship exists in both time and spatial domains. However, few of them can accurately deduce a time point when social media activities are highly affected by a rare event. Thus, it is difficult to characterize an...
Due to the emerging Big Data paradigm, traditional data management techniques result inadequate in many real life scenarios. In particular, the availability of huge amounts of data pertaining to social interactions among users calls for advanced analysis strategies. Furthermore, heterogeneity and high speed of this data require suitable data storage and management tools to be designed from scratch...
In this paper, we focus on the problem of group detection on Sina micro-blog, the most popular micro-blogging system in China. Efficiency plays an extremely important role in data analysis. As a consequence, we propose a framework to quickly detect groups, in which we modify SimHash algorithm to calculate similarity of short text and improve TF-IDF algorithm to accurately calculate the weights. Basing...
Community detection has become one of the most important methods for studying social networks. However, most of the existing community detection algorithms may not be applicable to mobile social networks due to their complexity. To solve this problem, we present a parallel algorithm to conduct community detection based on general stochastic block (GSB) model. We first model a mobile social network...
Recently, social event recommendation, which is to recommend a list of upcoming events to a user, has attracted a lot of research interests. In this paper, we first construct a heterogeneous graph to express the interactions among different types of entities in event-based social network. Based on the constructed graph, we propose a novel recommendation algorithm called reverse random walk with restart...
A social networking site such as Facebook, Twitter, and Linked In generates a terabyte of data. The Frequent Itemset Mining (FIM) is most well known technique to extract knowledge from data. Mining terabytes of data using Frequent Itemset Mining technique on a single computer is not efficient. MapReduce framework is used for mining such large data in a parallel manner. MRApriori, IMRApriori, BigFIM,...
In the last few years, the data generated by social networking systems have become interesting to analyze local and global social phenomena. A useful metric to identify influential people or opinion leaders is the between ness centrality index. The computation of this index is a very demanding task since its exact calculation exhibits O(nm) time complexity for unweighted graphs. This complexity has...
The MapReduce paradigm has become ubiquitous within Big Data Analytics. Within this field, Social Networks exist as an important area of applications as it relies on the large scale analysis of graphs. To enable the scalability of Social Networks, we consider the application of MapReduce design patterns for the determination of graph-based metrics. Specifically, we detail the application of a MapReduce-based...
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