File Name: community detection and mining in social media .zip
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The proposed survey discusses the topic of community detection in the context of Social Media. Community detection constitutes a significant tool for the analysis of complex networks by enabling the study of mesoscopic structures that are often associated with organizational and functional characteristics of the underlying networks. Community detection has proven to be valuable in a series of domains, e. However, despite the unprecedented scale, complexity and the dynamic nature of the networks derived from Social Media data, there has only been limited discussion of community detection in this context. More specifically, there is hardly any discussion on the performance characteristics of community detection methods as well as the exploitation of their results in the context of real-world web mining and information retrieval scenarios. To this end, this survey first frames the concept of community and the problem of community detection in the context of Social Media, and provides a compact classification of existing algorithms based on their methodological principles.
Social media mining is the process of obtaining big data from user-generated content on social media sites and mobile apps in order to extract patterns, form conclusions about users, and act upon the information, often for the purpose of advertising to users or conducting research. The term is an analogy to the resource extraction process of mining for rare minerals. Resource extraction mining requires mining companies to sift through vast quantities of raw ore to find the precious minerals; likewise, social media mining requires human data analysts and automated software programs to sift through massive amounts of raw social media data in order to discern patterns and trends relating to social media usage, online behaviours, sharing of content, connections between individuals, online buying behaviour, and more. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as these organizations can use these patterns and trends to design their strategies or introduce new programs, new products, processes or services. Social media mining uses a range of basic concepts from computer science , data mining , machine learning and statistics. Social media miners develop algorithms suitable for investigating massive files of social media data. Social media mining is based on theories and methodologies from social network analysis , network science , sociology , ethnography , optimization and mathematics.
Community detection and mining in social media pdf The past decade has witnessed the emergence of participatory Web and social media, bringing people together in many creative ways. Multi-media data, mining social networks and graph data, mining spatial. Social media, community detection, social media mining, centrality. Taxonomy of Community Detection Algorithms. Clustering, directed networks, complex networks, graph mining. Social networks, such as collaboration networks, sexual networks and interaction networks over.
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The past decade has witnessed the emergence of participatory Web and social media, bringing people together in many creative ways. Millions of users are playing, tagging, working, and socializing online, demonstrating new forms of collaboration, communication, and intelligence that were hardly imaginable just a short time ago. Social media also helps reshape business models, sway opinions and emotions, and opens up numerous possibilities to study human interaction and collective behavior in an unparalleled scale.
For a broad range of research and practical applications it is important to understand the allegiances, communities and structure of key players in society. One promising direction towards extracting this information is to exploit the rich relational data in digital social networks the social graph. As global social networks e. Distributing graph processing requires solving many difficult engineering problems, which has lead some researchers to look at single-machine solutions that are faster and easier to maintain. In this article, we present an approach for analyzing full social networks on a standard laptop, allowing for interactive exploration of the communities in the locality of a set of user specified query vertices.
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Не будучи религиозной, она не рассчитывала услышать ответ на свою молитву, но вдруг почувствовала внезапную вибрацию на груди и испуганно подскочила, однако тут же поняла: вибрация вовсе не была рукой Божьей - она исходила из кармана стратморовского пиджака. На своем Скайпейджере он установил режим вибрации без звонка, значит, кто-то прислал коммандеру сообщение. Шестью этажами ниже Стратмор стоял возле рубильника. В служебных помещениях ТРАНСТЕКСТА было черно как глубокой ночью. Минуту он наслаждался полной темнотой. Сверху хлестала вода, прямо как во время полночного шторма. Стратмор откинул голову назад, словно давая каплям возможность смыть с него вину.
Похоже, он принадлежал Филу Чатрукьяну. - Ты мне не веришь.
Возможно, это хорошо продуманный ход. Сьюзан попыталась осознать то, что ей сообщил коммандер. Она сомневалась, что Танкадо мог передать ключ какому-то человеку, который не приходился ему близким другом, и вспомнила, что в Штатах у него практически не было друзей. - Северная Дакота, - вслух произнесла она, пытаясь своим умом криптографа проникнуть в скрытый смысл этого имени.
Ты сам его и убил. Я все. - Довольно, Грег, - тихо сказал Стратмор.
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