Grants and Contributions:

Back to search

Title:

Data-centric Decision Supports for Large-scale Social Network Management

Agreement Number:

RGPIN

Agreement Value:

$100,000.00

Agreement Date:

May 10, 2017 -

Organization:

Natural Sciences and Engineering Research Council of Canada

Location:

Ontario, CA

Reference Number:

GC-2017-Q1-02796

Agreement Type:

Grant

Report Type:

Grants and Contributions

Additional Information:

Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023)

Recipient's Legal Name:

Li, Zhepeng (York University)

Program:

Discovery Grants Program - Individual

Program Purpose:

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.