Abstract
Influence maximization is a hot research topic in the social computing field and has gained tremendous studies motivated by its wild application scenarios. As the structures of social networks change over time, how to seek seed node sets from dynamic networks has attracted some attention. However, all of the existing studies were based on network topology structure data which have the limitations of high dimensionality and low efficiency. Aiming at this drawback, we first convert each node in the network to a low-dimensional vector representation by network representation learning and then solve the problem of dynamic influence maximization in the low-dimensional latent space. Comprehensive experiments on NetHEPT, Twitter, UCI, and Wikipedia datasets show that our method can achieve influence diffusion performance similar to state-of-the-art approaches in much less time.
Highlights
With the development of online social websites, information diffusion over social networks has become a new and important channel for network users to receive information
We develop dynamic influence maximization based on network representation learning, referred to as DIMNRL
The rest of this paper is organized as follows: In Section 2, we introduce the definition of the problem of maximizing the influence of dynamic social networks and the design of the DIMNRL method in detail
Summary
With the development of online social websites, information diffusion over social networks has become a new and important channel for network users to receive information. How to optimize and control the spread of information is an important problem in the field of social computing, and influence maximization is used to solve the above problem. Kempe et al [3] first proposed the discrete optimization method to solve the influence maximization problem. The greedy algorithm with approximate accuracy guarantee proposed by them takes too much time. Researchers proposed other greedy algorithms [5,6,7,8]. Chen et al [9] developed a degree discount heuristic algorithm (DegreeDiscountIC), which has the same performance as the greedy algorithm but greatly reduces the computation time. Chen proposed the LDAG heuristic algorithm [10] based on the directed acyclic graph and MIA based on the tree structure [11]. Wang et al [14] proposed CNCG considering an overlapping community structure [15, 16] and node coverage gain mechanism
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