Invitation to lecture by prof. Arunabha Sen

Date: 16.03.2015

Time: 13:15

Place: room 22, building C3

Speaker: – visiting prof. Arunabha Sen (employed in project ENGINE)

Presentation from the lecture: PDF

Video recording of lecture: Youtube

Topic: This will be a two part seminar, both of them will be about social networks

Part I Title: Influence Propagation in Adversarial Setting: How to Defeat Competition with Least Amount of Investment

Abstract: It has been observed that individuals’ decisions to adopt a product or innovation are often influenced by the recommendations of their friends and acquaintances. Motivated by this observation, the last few years have seen a number of studies on influence maximization in social networks. Theprimary goal of these studies is identification of the k most influential nodes in a network. A major limitation of these studies is that they focus on a non-adversarial environment, where only one player is engaged in influencing the nodes of a social network. However, in a realistic scenario there exist multiple players, each attempting to influence the nodes in a competitive fashion. The model in this paper considers a competitive environment where an unsubscribed node (a node that has not adopted any innovation or product) can adopt only one of the several competing products or innovations and once it adopts a product or innovation, it does not switch to another one. In this study we consider the scenario where the fi?rst player has already chosen a set of k nodes and the second player, with the knowledge of the choice of the fi?rst, attempts to identify a smallest set of nodes (excluding the ones already chosen by the fi?rst) so that when the influence propagation process stabilizes, the number of nodes influenced by the second player is larger than the number of nodes influenced by the fi?rst.

Part 2 Title: Spatio-Temporal Signal Recovery from Political Tweets in Indonesia

Abstract: Online social network community now provides an enormous volume of data for analyzing human sentiment about people, places, events and political activities. It is becoming increasingly clear that analysis of such data can provide great insights on the social, political and cultural aspects of the participants of these networks. As part of the Minerva project at Arizona State University, we have analyzed a large volume of Twitter data to understand radical political activity in the provinces of Indonesia. Based on analysis of radical/counter radical sentiments expressed in tweets by Twitter users, we create a Heat Map of Indonesia which visually demonstrates the degree of radical activities in various provinces of Indonesia. We create the Heat Map of Indonesia by computing (i) the Radicalization Index and (ii) the Location Index of each Twitter user from Indonesia, who has expressed some radical/counter-radical sentiment in her tweets. The conclusions derived from our analysis matches significantly with the analysis of Wahid Institute, a leading political think tank of Indonesia, thus validating our results.