@conference {1501143,
	title = {Explanations Systems for Influential Maximizations Algorithms},
	booktitle = {3rd International Workshop on Social Influence Analysis},
	year = {2017},
	abstract = {The field of influence maximization (IM) has made rapid advances, resulting in many sophisticated algorithms for identifying {\textquotedblleft}influential{\textquotedblright} members in social networks. However, in order to engender trust in IM algorithms, the rationale behind their choice of {\textquotedblleft}influential{\textquotedblright} nodes needs to be explained to its users. This is a challenging open problem that needs to be solved before these algorithms can be deployed on a large scale. This paper attempts to tackle this open problem via four major contributions: (i) we propose a general paradigm for designing explanation systems for IM algorithms by exploiting the tradeoff between explanation accuracy and interpretability; our paradigm treats IM algorithms as black boxes, and is flexible enough to be used with any algorithm; (ii) we utilize this paradigm to build XplainIM, a suite of explanation systems; (iii) we illustrate the usability of XplainIM by explaining solutions of HEALER (a recent IM algorithm) among \~{}200 human subjects on Amazon Mechanical Turk (AMT); and (iv) we provide extensive evaluation of our AMT results, which shows the effectiveness of XplainIM.},
	author = {Amulya Yadav and Rahmattalabi, Aida and Ece Kamar and Vayanos, Phebe and Tambe, Milind and Noronha, Venil Loyd}
}
