We consider the following advertisement problem in online social networks (OSNs). Given a fixed advertisement investment, e.g., a number of free samples that can be given away to a small number of users, a company needs to determine the probability that users in the OSN will eventually purchase the product. In this paper, we model OSNs as scale-free graphs (either with or without high clustering coefficient). We employ various influence mechanisms that govern the influence spreading in such large-scale OSNs and use the local mean field (LMF) technique to analyze these online social networks wherein states of nodes can be changed by various influence mechanisms. We extend our model for advertising with multiple rating levels. Extensive simulations are carried out to validate our models, which can provide insight on designing efficient advertising strategies in online social networks.

In peer-to-peer (P2P) networks, incentive protocol is used to encourage cooperation among end-nodes so as to deliver a scalable and robust service. However, the design and analysis of incentive protocols have been ad hoc and heuristic at best. The objective of this paper is to provide a simple yet general framework to analyze and design incentive protocols. We consider a class of incentive protocols that can learn and adapt to other end-nodes' strategies. Based on our analytical framework, one can evaluate the expected performance gain and, more importantly, the system robustness of a given incentive protocol. To illustrate the framework, we present two adaptive learning models and three incentive policies and show the conditions in which the P2P networks may collapse and the conditions in which the P2P networks can guarantee a high degree of cooperation. We also show the connection between evaluating incentive protocol and evolutionary game theory so one can easily identify robustness characteristics of a given policy. Using our framework, one can gain the understanding on the price of altruism and system stability, as well as the correctness of the adaptive incentive policy.

Data-driven P2P streaming systems can potentially provide good playback rate to a large number of viewers. One important design problem in such P2P systems is to determine the optimal chunk selection policy that provides high continuity playback under the server's upload capacity constraint. We present a general and unified mathematical framework to analyze a large class of chunk selection policies. The analytical framework is asymptotically exact when the number of viewers is large. More importantly, we provide some interesting observations on the optimal chunk selection policy: it is of shaped and becomesmore greedy as the upload capacity of the server increases. This insight helps content providers to deploy large scale streaming systems with a QoS-guarantee under a given cost constraint.