Selecting Influential Features by a Learnable Content-Aware Linear Threshold Model

Ansh Khurana, Alvis Logins, Panagiotis Karras

Abstract: Consider a network in which items propagate in a manner determined by their inherent characteristics or features. How should we select such inherent content features of a message emanating from a given set of nodes, so as to engender high influence spread over the network? This influential feature set selection problem has received scarce attention, contrary to its dual, influential node set selection counterpart, which calls to select the initial adopter nodes from which a fixed message emanates, so as to reach high influence. However, the influential feature set selection problem arises in many practical settings, where initial adopters are given, while propagation depends on the perception of certain malleable message features. We study this problem for a diffusion governed by a content-aware linear threshold (CALT) model, by which, once the aggregate weight of influence on a node exceeds a randomly chosen threshold, the item goes through. We show that the influence spread function is not submodular, hence a greedy algorithm with approximation guarantees is inadmissible. We propose a method that learns the parameters of the CALT model and adapt the SimPath diffusion estimation method to build a heuristic for the influential feature selection problem. Our experimental study demonstrates the efficacy and efficiency of our technique over synthetic and real data.