With the rapid growth of social networking services, there has been an explosion in the area of viral marketing research. The idea is to explore the marketing value of social networks with respect to increasing the adoption of a new innovation/product, or generating brand awareness. A common technique employed is to target a small set of users that will result in a large cascade of further adoptions. Existing formulations and solutions in the literature generally focus on the case of a single company. Yet, the problem gets more challenging if there are a number of companies (the advertisers), each one aiming to create a viral advertising campaign of its own by paying a set of network users (the endorsers). The endorsers are asked to post intriguing and entertaining ad messages that contain the content selected by the advertising company. The advertiser has a predefined budget on how much it is going to spend on this effort. Also each endorser has a limit on the number of companies for which it serves as an endorser. In this thesis, we design a broker system as an intermediary between advertisers and endorsers. We seek to maximize the spread of advertisements over regular users (the audience), while considering the budget constraints of advertisers. Our system avoids overburdening of the endorsers and overloading of the audience. We model the problem through a combinatorial optimization framework with budget constraints. We develop a cost-effective algorithm called CEAL, which is designed for solving the problem with close to optimal performance on large-scale graphs. We also revisit the traditional Independent Cascade Model (ICM) to account for overloaded users. We propose an extension of ICM called Independent Cascade Model with Overload (ICMO). We study the influence maximization problem on variations of this model. We perform experiments over multiple real-world social networks and empirically show that the proposed CEAL algorithm performs close to optimal in terms of coverage, yet is sufficiently lightweight to execute on large-scale graphs.