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Title
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IMaxer : a unified system for evaluating influence maximization in location-based social networks
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Author
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Abstract
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| Due to the popularity of social networks with geo-tagged activities, so-called location-based social networks (LBSN), a number of methods have been proposed for influence maximization for applications such as word-of-mouth marketing (WOMM), and out-of-home marketing (OOH). It is thus important to analyze and compare these different approaches. In this demonstration, we present a unified system IMaxer that both provides a complete pipeline of state-of-the-art and novel models and algorithms for influence maximization (IM) as well as allows to evaluate and compare IM techniques for a particular scenario. IMaxer allows to select and transform the required data from raw LBSN datasets. It further provides a unified model that utilizes interactions of nodes in an LBSN, i.e., users and locations, for capturing diverse types of information propagations. On the basis of these interactions, influential nodes can be found and their potential influence can be simulated and visualized using Google Maps and graph visualization APIs. Thus, IMaxer allows users to compare and pick the most suitable IM method in terms of effectiveness and cost. |
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Language
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English
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Source (book)
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CIKM '17 Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 06-10 November 2017, Singapore
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Publication
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New york
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Assoc computing machinery
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2017
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ISBN
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978-1-4503-4918-5
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978-1-4503-4918-5
978-1-4503-4918-5
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DOI
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10.1145/3132847.3133184
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Volume/pages
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(2017)
, p. 2523-2526
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ISI
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000440845300333
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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