Influence Maximization Based on Snapshot Prediction in Dynamic Online Social Networks

Abstract

With the vigorous development of the mobile Internet, online social networks have greatly changed the way of life of human beings. As an important branch of online social network research, influence maximization refers to finding K nodes in the network to form the most influential seed set, which is an abstract model of viral marketing. Most of the current research is based on static network structures, ignoring the important feature of network structures changing with time, which discounts the effect of seed nodes in dynamic online social networks. To address this problem in dynamic online social networks, we propose a novel framework called Influence Maximization based on Prediction and Replacement (IMPR). This framework first uses historical network snapshot information to predict the upcoming network snapshot and then mines seed nodes suitable for the dynamic network based on the predicted result. To improve the computational efficiency, the framework also adopts a fast replacement algorithm to solve the seed nodes between different snapshots. The scheme we adopted exhibits four advantages. First, we extended the classic influence maximization problem to dynamic online social networks and give a formal definition of the problem. Second, a new framework was proposed for this problem and a proof of the solution is given in theory. Third, other classical algorithms for influence maximization can be embedded into our framework to improve accuracy. More importantly, to reveal the performance of the scheme, a series of experiments based on different settings on real dynamic online social network datasets were carried out, and the experimental results are very promising.

An Influence Maximization Algorithm Based on Improved K-Shell in Temporal Social Networks

“…In addition, Zhang et al [4] propose a prediction and replacement based influence maximization framework using machine learning techniques, which first predicts upcoming network snapshots using historical network snapshot information, and then mines seed nodes suitable for dynamic networks based on the prediction results. Chandran et al [5] study temporal influence maximization on dynamic social networks and proposes a dynamic influence based seed selection method which estimates the influence of each node by introducing a two-hop triangular influence.…”

“…Some studies [4,5] have modeled temporal networks by snapshots to solve the IMT problem, but these methods are too cumbersome when the network size is large. However, Kitsak et al [6] find that the number of cores has more stable propagation than node attributes such as degree and betweenness centrality.…”

Influence Maximization Dynamics and Topological Order on Erdös-Rényi Networks

“…Clearly, the topological entropy of the Erdös-Rényi network G (n,p) is characterized by the expected value of the node degree δ(x i ) ∼ = np, with p satisfying Equation (7) and n being sufficiently large. Therefore, this topological invariant is dependent on the network size n and the connection probability p, which reflects the topology of the Erdös-Rényi networks under analysis.…”

“…These propagation processes are referred to as social contagion, as they are reminiscent of how a disease is transmitted between the individuals in a population. Several authors have presented different approaches to this theme over the past decades; see [7] for a complete and updated compilation of references in this field.…”

“…Several of these works consider the dynamics in the information dissemination process, such as the dynamic activation probability, dynamic threshold, and dynamic perception, while others consider the dynamics of the network topology as links are added or removed over time. On this subject, see [7,[10][11][12].…”