The Mathematics of Infectious Diseases
SIAM Review
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The sybil attack in sensor networks: analysis & defenses
Proceedings of the 3rd international symposium on Information processing in sensor networks
SybilGuard: defending against sybil attacks via social networks
IEEE/ACM Transactions on Networking (TON)
Approximation Algorithms for the Firefighter Problem: Cuts over Time and Submodularity
ISAAC '09 Proceedings of the 20th International Symposium on Algorithms and Computation
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We study infiltration of a trust/reputation based network. At every time step, the agent solicits a connection request (friend request). The goal of the agent is to amass as many such connections as possible to further its goals. Our model for such an infiltration of a network relies on two properties of the actors in the network. They desire more links (an ego effect); they are more likely to connect to trusted or credible nodes (the trust by reference effect). We demonstrate the following proporties of this infiltration. (i) The trust by reference effect is critical. If agents are not trusting enough, then the network is robust to infiltration; however, with logarithmically more trust, the process phase transitions to significant infiltration. (ii) The network structure is important. If the trust effect is small, then well clustered networks (typical social networks) are easier to infiltrate; when the trust effect is larger, then networks with large expansion (for example Erdös-Rényi random graphs) are easier to infiltrate. (iii) The algorithm used by the agent plays a significant role in success of the infiltration. Random connection requests are much less successful than even simple greedy strategies, even if those greedy strategies are restricted to only using local information.