IM aims to extract a given number of users that maximize the influence spread over a network. Previous efforts on. IM can be generally categorized into static ...
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 ...
Feb 6, 2017 · Abstract:Influence maximization (IM), which selects a set of k users (called seeds) to maximize the influence spread over a social network, ...
To track the influential users over social streams in real-time, we propose a Stream Influence Maximization. (SIM) query which is formally defined as follows ...
Oct 22, 2024 · Influence maximization (IM), which selects a set of $k$ users (called seeds) to maximize the influence spread over a social network, ...
Real-Time Influence Maximization on Dynamic Social Streams
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A novel IM query named Stream Influence Maximization (SIM), which adopts the sliding window model and maintains a set of k seeds with the largest influence ...
Feb 6, 2017 · ABSTRACT. Influence maximization (IM), which selects a set of k users. (called seeds) to maximize the influence spread over a social.
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In this paper, we study the problem of maximizing influence diffusion in dynamic social networks, ie networks that change over time.
In this paper, we study the problem of maximizing influence diffusion in a dynamic social network. Specifically, the network changes over time and the changes ...
Experimental results on real-world datasets demonstrate the effectiveness and efficiency of the proposed frameworks against the state-of-the-art IM algorithms.