The steady growth of graph data from social networks has resulted in wide-spread research in finding solutions to the influence maximization (IM) problem. This results in extension of the state-of-the-art almost every year. With the recent explosion in the application of IM in solving real-world problems, it is no longer a theoretical exercise. IM is employed by all-and-sundry, with OnePlus series of mobile phones and Hokey Pokey ice-creams being the most prominent industrial use-cases. Given this scenario, navigating the maze of IM techniques to get an in-depth understanding and their utilities is of prime importance. In this tutorial, we address this paramount issue and solve the dilemma of “Which IM technique to use and under What scenarios”? “What does it really mean to claim to be the state-of-the-art”?

This tutorial builds upon our benchmarking study and will provide a concise and intuitive overview of the most important IM techniques, which is usually lost in the technical literature. More fundamentally, we will unearth a series of incorrect claims made by prominent IM papers, disseminate the inherent deficiencies of existing approaches and surface the open challenges in IM even after a decade of research.

Dr. Akhil Arora

American Express Big Data Labs, Bangalore

Dr. Sainyam Galhotra

University of Massachusetts, Amherst, USA

Dr. Sayan Ranu

IIT Delhi

Dr. Shourya Roy

American Express Big Data Labs, Bangalore