TY - GEN
T1 - IMaxer
T2 - 26th ACM International Conference on Information and Knowledge Management, CIKM 2017
AU - Saleem, Muhammad Aamir
AU - Kumar, Rohit
AU - Calders, Toon
AU - Xie, Xike
AU - Pedersen, Torben Bach
PY - 2017/11/6
Y1 - 2017/11/6
N2 - 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-ofthe-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.
AB - 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-ofthe-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.
UR - http://www.scopus.com/inward/record.url?scp=85037363376&partnerID=8YFLogxK
U2 - 10.1145/3132847.3133184
DO - 10.1145/3132847.3133184
M3 - Article in proceeding
AN - SCOPUS:85037363376
T3 - Conference on Information and Knowledge Management
SP - 2523
EP - 2526
BT - CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery
Y2 - 6 November 2017 through 10 November 2017
ER -