TY - GEN
T1 - Maximizing influence in competitive environments
T2 - 2nd International Conference on Decision and Game Theory for Security, GameSec 2011
AU - Clark, Andrew
AU - Poovendran, Radha
PY - 2011
Y1 - 2011
N2 - Ideas, ranging from product preferences to political views, spread through social interactions. These interactions may determine how ideas are adopted within a market and which, if any, become dominant. In this paper, we introduce a model for Dynamic Influence in Competitive Environments (DICE). We show that existing models of influence propagation, including linear threshold and independent cascade models, can be derived as special cases of DICE. Using DICE, we explore two scenarios of competing ideas, including the case where a newcomer competes with a leader with an already-established idea, as well as the case where multiple competing ideas are introduced simultaneously. We formulate the former as a Stackelberg game and the latter as a simultaneous-move game of complete information. Moreover, we show that, in both cases, the payoff functions for both players are submodular, leading to efficient algorithms for each player to approximate his optimal strategy. We illustrate our approach using the Wiki-vote social network dataset.
AB - Ideas, ranging from product preferences to political views, spread through social interactions. These interactions may determine how ideas are adopted within a market and which, if any, become dominant. In this paper, we introduce a model for Dynamic Influence in Competitive Environments (DICE). We show that existing models of influence propagation, including linear threshold and independent cascade models, can be derived as special cases of DICE. Using DICE, we explore two scenarios of competing ideas, including the case where a newcomer competes with a leader with an already-established idea, as well as the case where multiple competing ideas are introduced simultaneously. We formulate the former as a Stackelberg game and the latter as a simultaneous-move game of complete information. Moreover, we show that, in both cases, the payoff functions for both players are submodular, leading to efficient algorithms for each player to approximate his optimal strategy. We illustrate our approach using the Wiki-vote social network dataset.
KW - influence propagation
KW - noncooperative game
KW - Social network
UR - https://www.scopus.com/pages/publications/81755178419
U2 - 10.1007/978-3-642-25280-8_13
DO - 10.1007/978-3-642-25280-8_13
M3 - Conference contribution
AN - SCOPUS:81755178419
SN - 9783642252792
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 151
EP - 162
BT - Decision and Game Theory for Security - Second International Conference, GameSec 2011, Proceedings
Y2 - 14 November 2011 through 15 November 2011
ER -