Tutorial 3

Title: Influence Learning and Maximization

Description 

The problem of maximizing or minimizing the spreading in a social network has become more timely than ever with the advent of fake news and the coronavirus epidemic. The solution to this problem pertains to influence maximization algorithms that identify the right nodes to lockdown for epidemic containment, hire for viral marketing campaigns, block for online political propaganda etc. Though these algorithms have been developed for many years, the majority of the literature focuses on scalability issues and relaxing the method’s assumptions. In the recent years, the emergence of new complementary data and more advanced machine learning methods for decision have guided part of the literature towards learning-based approaches. These can range from learning how information spreads over a network, to learning how to solve the combinatorial optimization problem itself. In this tutorial, we aim to dissentangle and clearly define the different tasks around learning for influence applications in social networks. More specifically, we start from traditional influence maximization algorithms, describe the need of influence estimation and delineate the state-of-the-art on influence and diffusion learning. Subsequently, we delve into the problem of learning while optimizing the influence spreading which is based on online learning algorithms. Finally, we describe the latest approaches on learning influence maximization with graph neural networks and deep reinforcement learning.

Authors’ Short Biography :

George Panagopoulos is a PhD candidate at LIX, Ecole Polytechnique, France.
He received his M.S. in Computer Science from University of Houston, US (2016), for which he received the best M.S. thesis award from the respective department. At that time, he was a research assistant at the Computational Physiology Lab and prior to that, at the Software Knowledge and Engineering Lab, NCSR Demokritos in Athens, Greece. His research interests lie in machine learning and its application to network science. He has recently published two conference papers on influence maximization, one of which received an honorable mention for the best paper at ICWSM 2020, and one relevant journal paper at TKDE.

Fragkiskos Malliaros is an Assistant Professor at Paris-Saclay University, CentraleSupélec and associate researcher at Inria Saclay. He also co-directs the M.Sc. Program in Data Sciences and Business Analytics (CentraleSupélec and ESSEC Business School). Right before that, he was a postdoctoral researcher at UC San Diego (2016-17) and at École Polytechnique (2015-16). He received his Ph.D. in Computer Science from École Polytechnique (2015) and his M.Sc. degree from the University of Patras, Greece (2011). He is the recipient of the 2012 Google European Doctoral Fellowship in Graph Mining, the 2015 Thesis Prize by École Polytechnique, and best paper awards at TextGraphs-NAACL 2018 and ICWSM 2020 (honorable mention). In the past, he has been the co-chair of various data science-related workshops, and has also presented ten invited tutorials at international conferences in the area of graph mining and data science (e.g., ICDM, WSDM, WWW, EDBT). His research interests span the broad area of data science, with focus on graph mining, machine learning and network analysis. He has co-authored the aforementioned publications while he has also published extensively on influencer identification techniques.

Duration:  Half-Day