Reference number : GC-2017-Q1-02796, amendment number : , agreement type : G, recipient legal name : Li, Zhepeng (York University)|Li, Zhepeng (Université York), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Growing interest in the value of digital information, commonly known as data, creates opportunities to re-invent managerial machinery and lead to the changes in decision making. A much desired means to gain value from data stems from decision support (DS), which provides information that supports users and organizations in decision-making activities. To embrace the advent of big data, decision support needs to address challenges in discovering actionable knowledge when the problem scales up and uncertainty arises substantially. Data-centric approaches are advocated to cope with these issues. 
 
This proposal is intended to investigate a theme of decision support problems that caters to the needs of effective management in large-scale social networks. The formulated problems are to be addressed by data-centric approaches integrating machine learning, optimization, and heuristic methods. In the long run, it aims at achieving a comprehensive DS toolbox for effective social network management and governance. As for the short-term goal, it is to focus on the predictive stage that addresses actionable knowledge for decision support problems in social network formation, network-based propagation, and social recommendation. 
 
In Canada, a large portion of the population is engaged with online social networking services. At the same time, the business of social networks is estimated to grow considerably. The proposed research will generate benefits in terms of both commercial and societal interests. Methods that are developed in the proposed research can be applied in a wide range of domains, in that the large network can conveniently adapt to account for financial interactions, world-wide web, genetic graph, and terrorism relationships. The focal research would thus accomplish external impacts by addressing related problems, such as credit/risk evaluations, WWW navigability, identification of hidden protein interactions, and terrorism/riots surveillance/containment, which range from finance, IT, biology to sociology., program purpose french : Growing interest in the value of digital information, commonly known as data, creates opportunities to re-invent managerial machinery and lead to the changes in decision making. A much desired means to gain value from data stems from decision support (DS), which provides information that supports users and organizations in decision-making activities. To embrace the advent of big data, decision support needs to address challenges in discovering actionable knowledge when the problem scales up and uncertainty arises substantially. Data-centric approaches are advocated to cope with these issues. 
 
This proposal is intended to investigate a theme of decision support problems that caters to the needs of effective management in large-scale social networks. The formulated problems are to be addressed by data-centric approaches integrating machine learning, optimization, and heuristic methods. In the long run, it aims at achieving a comprehensive DS toolbox for effective social network management and governance. As for the short-term goal, it is to focus on the predictive stage that addresses actionable knowledge for decision support problems in social network formation, network-based propagation, and social recommendation. 
 
In Canada, a large portion of the population is engaged with online social networking services. At the same time, the business of social networks is estimated to grow considerably. The proposed research will generate benefits in terms of both commercial and societal interests. Methods that are developed in the proposed research can be applied in a wide range of domains, in that the large network can conveniently adapt to account for financial interactions, world-wide web, genetic graph, and terrorism relationships. The focal research would thus accomplish external impacts by addressing related problems, such as credit/risk evaluations, WWW navigability, identification of hidden protein interactions, and terrorism/riots surveillance/containment, which range from finance, IT, biology to sociology., agreement title english : Data-centric Decision Supports for Large-scale Social Network Management, agreement title french : Data-centric Decision Supports for Large-scale Social Network Management, agreement number : RGPIN, agreement value : 100000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada