Title:

Content Selection for Viral Network Influence

Abstract:

How do we select content that will become viral in a whole network after we share it with friends or followers? Significant research activity has been dedicated to the problem of strategically selecting a seed set of initial adopters so as to maximize a meme’s spread in a network. Yet this line of work assumes that the success of such a campaign depends solely on the choice of a tunable set of initiators, regardless of how users perceive the propagated meme, which is fixed. Yet in many real-world settings, the opposite holds: a meme’s propagation depends on users’ perceptions of its tunable characteristics, while the set of initiators is fixed.

We address the natural problem that arises in such circumstances:

Suggest content, expressed as a limited set of attributes, for a creative promotion campaign that starts out from a given seed set of initiators, so as to maximize its expected spread over a social network.

To our knowledge, no previous work addresses this problem. We find that the problem is NP-hard and inapproximable. As a tight approximation guarantee is not admissible, we design an efficient heuristic, Explore-Update, as well as a conventional Greedy solution. Our experimental evaluation demonstrates that Explore-Update selects near-optimal attribute sets with real data, achieves 30% higher spread than baselines, and runs an order of magnitude faster than Greedy.

Bio:

Panagiotis Karras (Panos) is an Associate Professor of Computer Science at Aalborg University. He earned a Ph.D. in Computer Science from the University of Hong Kong and an M. Eng. in Electrical and Computer Engineering from the National Technical University of Athens. He has held positions at the Skolkovo Institute of Science and Technology, Rutgers Business School, the National University of Singapore, the University of Zurich, and the Technical University of Denmark. Panos'

interests are in the confluence of data management, data mining, and database security. He has published over 50 research articles and received over 1500 citations. He has been awarded with the 2008 Hong Kong Young Scientist Award; supported by Singapore’s Lee Kuan Yew Endowment Fund; and has worked with the MIT Skoltech Initiative. He regularly serves as a program committee member and referee for the major international conferences and journals in the above areas.