Predicting when an individual will adopt a new behavior is an important problem in application domains such as marketing and public health. This paper examines the performance of a wide variety of social network based measurements proposed in the literature — which have not been previously compared directly. We study the probability of an individual becoming influenced based on measurements derived from neighborhood (i.e. number of influencers, personal network exposure), structural diversity, locality, temporal measures, cascade measures, and metadata. We also examine the ability to predict influence based on choice of classifier and how the ratio of positive to negative samples in both training and testing affect prediction results — further enabling practical use of these concepts for social influence applications.
['Nikhil Kumar', 'Ruocheng Guo', 'Ashkan Aleali', 'Paulo Shakarian']
An empirical evaluation of social influence metrics