  Can we develop a computer algorithm that assesses the creativity of a
painting given its context within art history? This paper proposes a novel
computational framework for assessing the creativity of creative products, such
as paintings, sculptures, poetry, etc. We use the most common definition of
creativity, which emphasizes the originality of the product and its influential
value. The proposed computational framework is based on constructing a network
between creative products and using this network to infer about the originality
and influence of its nodes. Through a series of transformations, we construct a
Creativity Implication Network. We show that inference about creativity in this
network reduces to a variant of network centrality problems which can be solved
efficiently. We apply the proposed framework to the task of quantifying
creativity of paintings (and sculptures). We experimented on two datasets with
over 62K paintings to illustrate the behavior of the proposed framework. We
also propose a methodology for quantitatively validating the results of the
proposed algorithm, which we call the "time machine experiment".
