AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Social networks, incentives, and search
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
On the submodularity of influence in social networks
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Reconstruction for Models on Random Graphs
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Markov Chains and Stochastic Stability
Markov Chains and Stochastic Stability
Patterns of influence in a recommendation network
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Hi-index | 0.00 |
Motivated by the recent emergence of large online social networks, we seek to understand the effects the underlying social network (graph) structure and the information dynamics have on the creation of influence of an individual. We examine a natural model for information dynamics under two important temporal scales: a first impression setting and a long- term or equilibrated setting. We obtain a characterization of relevant network structures under these temporal aspects, thereby allowing us to formalize the existence of influential agents. Specifically, we find that the existence of an influential agent corresponds to: (a) strictly positive information theoretic capacity over an infinite-sized noisy broadcast tree network in the first impression case, and (b) positive recurrent property of an appropriate (countable state space) Markov chain in the long-term case. As an application of our results, we evaluate the parameter space of the popular "small world" network model to identify when the network structure supports the existence of influential agents.