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An influence maximization algorithm based on low-dimensional representation learning
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DOI：10.1007/s10489-022-03178-z.png)
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Influence maximization (IM) problem is to choose k seed nodes in the social network to maximize the number of nodes that can be affected. As an essential part of the social network analysis, the influence maximization problem has been extensively investigated. Most existing algorithms usually set the activation probability to a fixed value or reciprocal of the in-degree. However, this operation is not accurate because the activation probability is more complicated than those mentioned above in the real social networks. To solve this problem, this paper proposes a new algorithm called Low-Dimensional Representation Learning for IM (LDRLIM) to address the IM problem. The LDRLIM utilizes Discount-degree Descending (DED) search strategy to generate the candidate nodes set DOUBLE-STRUCK CAPITAL C and IC Walk to obtain the influence context of the candidate nodes. The LDRLIM algorithm learns the low-dimensional embedding vectors of influencers and susceptible nodes according to a Multi-task Neural Network Low-dimensional Representation learning model (MNNLR). Afterwards, the Similarity Influence (SI) of the node is obtained according to the representation vectors of the nodes. The LDRLIM algorithm employs SI to consider the potential influence relationship between nodes by embedding vector. Furthermore, the Cost-Effective Lazy Forward (CELF) strategy is used to accelerate the process of selecting the influential nodes, which avoids a large amount of model simulation time to improve efficiency. Therefore, the proposed LDRLIM algorithm is suitable for large-scale social networks. The experimental results on seven real-world datasets indicate that the LDRLIM significantly outperforms other comparison algorithms.
Keyword:
Social networks
Influence maximization
Representation learning
Embedding model
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3.5
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7.6K
被引数:
1.7W
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