Influence Maximization (IM) on Social Networks: The State-of-the-Art and the Gaps that Remain

Influence maximization (IM) on social networks is one of the most active areas of research in computer science. While various IM techniques proposed over the last decade have definitely enriched the field, unfortunately, experimental reports on existing techniques fall short in validity and integrity since many comparisons are not based on a common platform or merely discussed in theory. In this paper, we perform an in-depth benchmarking study of IM techniques on social networks. Specifically, we design a benchmarking platform, which enables us to evaluate and compare the existing techniques systematically and thoroughly under identical experimental conditions. Our benchmarking results analyze and diagnose the inherent deficiencies of the existing approaches and surface the open challenges in IM even after a decade of research. More fundamentally, we unearth and debunk a series of myths and establish that there is no single state-of-the-art technique in IM. At best, a technique is the state of the art in only one aspect.
Akhil Arora is a Researcher in the Text & Graph Analytics group at Xerox Research Centre India (XRCI). His research interests include databases and data mining, more specifically graph mining, and machine learning. At XRCI, he works as a leading contributor in designing novel graph algorithms for real-world problems. He holds a Masters degree in computer science from the Indian Institute of Technology (IIT), Kanpur. He has published his works in leading data mining and database conferences and also regularly serves as a reviewer in these conferences.

Akhil can be contacted at [email protected]. More details at: http://www.cse.iitk.ac.in/users/aarora/