Real-time influence maximization on dynamic social streams

Abstract

Influence maximization (IM), which selects a set of k users (called seeds) to maximize the influence spread over a social network, is a fundamental problem in a wide range of applications such as viral marketing and network monitoring. Existing IM solutions fail to consider the highly dynamic nature of social influence, which results in either poor seed qualities or long processing time when the network evolves. To address this problem, we define a novel IM query named Stream Influence Maximization (SIM) on social streams. Technically, SIM adopts the sliding window model and maintains a set of k seeds with the largest influence value over the most recent social actions. Next, we propose the Influential Checkpoints (IC) framework to facilitate continuous SIM query processing. The IC framework creates a checkpoint for each window slide and ensures an ε-approximate solution. To improve its efficiency, we further devise a Sparse Influential Checkpoints (SIC) framework which selectively keeps O( -approximate solution. Experimental results on both real-world and synthetic datasets confirm the effectiveness and efficiency of our proposed frameworks against the state-of-the-art IM approaches.

Human-Centric Cyber Social Computing Model for Hot-Event Detection and Propagation

“…Literature in the context of event detection demonstrates that the influence of users in the social network is related to the relationship between users and topic while the influence of the same user is dissimilar under different topics [30]. Furthermore, these studies in [11], [23], [31], [32] focus on the effect of topic influence on user activation probability and ignored utilizing the popularity of topics, links between posts, and diffusion power of the users. This leads to the waste of users influence resulting in lower efficiency and the improper number of final mining core users.…”

Automated Influence Maintenance in Social Networks: An Agent-based Approach

“…Subbian et al propose an influencequery framework to mine influencers in a time-sensitive fashion from streaming social data [20]. Wang et al propose the Influential Checkpoints framework and a Sparse Influence Checkpoints framework to tackle the stream influence maximization querying processing [21].…”

Coresets for Minimum Enclosing Balls over Sliding Windows

“…In addition, Smooth Histograms are also used for submodular maximization in the sliding window model [16, ‫ܘ‬ ଶ ‫ܘ‬ ଵ ‫ܘ‬ ଷ ‫ܘ‬ ସ Figure 3: An illustration of Example 1. 29,30]. Nevertheless, such an extension is still not applicable for our problem because the radius function r * (·) is not submodular in the view of set functions, which is shown by Example 1.…”