# Real-Time Influence Maximization on Dynamic Social Streams

## TL;DR

This paper introduces a real-time influence maximization method for dynamic social streams, using a novel checkpoint framework to efficiently identify influential users in evolving social networks.

## Contribution

It proposes the SIM query model and the IC/SIC frameworks, enabling efficient, approximate influence maximization on social streams with dynamic network updates.

## Key findings

- The SIC framework reduces checkpoint storage to O(log N/β).
- Experimental results show improved efficiency over existing methods.
- The approach maintains high influence spread accuracy in dynamic settings.

## 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 $\varepsilon$-approximate solution. To improve its efficiency, we further devise a Sparse Influential Checkpoints (SIC) framework which selectively keeps $O(\frac{\log{N}}{\beta})$ checkpoints for a sliding window of size $N$ and maintains an $\frac{\varepsilon(1-\beta)}{2}$-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.