Online social networks (OSNs) have become a powerful medium of communicating, sharing and disseminating information. Because of popularity and availability of OSNs throughout the world, the connected users can spread information faster and thus propagate influence over each other constantly. Due to such impact, a lot of applications on OSNs focused on picking an initial set of users (seeds) to infuse their message in the OSN. Due to huge size of the network, the main challenge in picking the initial set is to maximize the resultant influence over the users in the network. The optimization problem of finding out the most influential set of members in an OSN for maximization of influence is an NP-hard problem. In this paper, we propose using the Positive Influential Dominating Set (PIDS) algorithm for the initial seed. PIDS is a well-known algorithm which determines the influential backbone nodes in the networks. We implemented PIDS-based influence maximization by using different propagation models. We compared PIDS performance to that of the existing approaches based on greedy and random heuristics. The experimental results from extensive simulation on real-world network data sets show that PIDS gives better influence spread than greedy and random for both Independent Cascade Model and Linear Threshold Model of influence propagation. PIDS is also scalable to large networks and in all size ranges, it performs well in influence maximization.

With online social networks such as Facebook and Twitter becoming globally popular, there is renewed interest in understanding the structural and dynamical properties of social networks. In this thesis we study several stochastic models arising in the context of the spread of influence or information in social networks. Our objective is to provide compact and accurate quantitative descriptions of the spread processes, to understand the effects of various system parameters, and to design policies for the control of such diffusions. One of the well established models for influence spread in social networks is the threshold model. An individual’s threshold indicates the minimum level of “influence” that must be exerted, by other members of the population engaged in some activity, before the individual will join the activity. We begin with the well-known Linear Threshold (LT) model introduced by Kempe et al. [1]. We analytically characterize the expected influence for a given initial set under the LT model, and provide an equivalent interpretation in terms of acyclic path probabilities in a Markov chain. We derive explicit optimal initial sets for some simple networks and also study the effectiveness of the Pagerank [2] algorithm for the problem of influence maximization. Using insights from our analytical characterization, we then propose a computationally efficient G1-sieving algorithm for influence maximization and show that it performs on par with the greedy algorithm, through experiments on a coauthorship dataset. The Markov chain characterisation gives only limited insights into the dynamics of influence spread and the effects of the various parameters. We next provide such insights in a restricted setting, namely that of a homogeneous version of the LT model but with a general threshold distribution, by taking the fluid limit of a probabilistically scaled version of the spread Markov process. We observe that the threshold distribution features in the fluid limit via its hazard function. We study the effect of various threshold distributions and show that the influence evolution can exhibit qualitatively different behaviors, depending on the threshold distribution, even in a homogeneous setting. We show that under the exponential threshold distribution, the LT model becomes equivalent to the SIR (Susceptible-Infected-Recovered) epidemic model [3]. We also show how our approach is easily amenable to networks with heterogeneous community structures. Hundreds of millions of people today interact with social networks via their mobile devices. If the peer-to-peer radios on such devices are used, then influence spread and information spread can take place opportunistically when pairs of such devices come in proximity. In this context, we develop a framework for content delivery in mobile opportunistic networks with joint evolution of content popularity and availability. We model the evolution of influence and content spread using a multi-layer controlled epidemic model, and, using the monotonicity properties of the o.d.e.s, prove that a time-threshold policy for copying to relay nodes is delay-cost optimal. Information spread occurs seldom in isolation on online social networks. Several contents might spread simultaneously, competing for the common resource of user attention. Hence, we turn our attention to the study of competition between content creators for a common population, across multiple social networks, as a non-cooperative game. We characterize the best response function, and observe that it has a threshold structure. We obtain the Nash equilibria and study the effect of cost parameters on the equilibrium budget allocation by the content creators. Another key aspect to capturing competition between contents, is to understand how a single end-user receives and processes content. Most social networks’ interface involves a timeline, a reverse chronological list of contents displayed to the user, similar to an email inbox. We study competition between content creators for visibility on a social network user’s timeline. We study a non-cooperative game among content creators over timelines of fixed size, show that the equilibrium rate of operation under a symmetric setting, exhibits a non-monotonic behavior with increasing number of players. We then consider timelines of infinite size, along with a behavioral model for user’s scanning behavior, while also accounting for variability in quality (influence weight) among content creators. We obtain integral equations, that capture the evolution of average influence of competing contents on a social network user’s timeline, and study various content competition formulations involving quality and quantity.