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A new multi-wave continuous action-set cellular learning automata for link prediction problem in weighted multi-layer social networks

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Abstract

One of the main research areas in social network analysis (SNA) is link prediction (LP). Social networks can be shown as a graph, and LP algorithms predict future links using the previous structure of the social network. A significant part of previous studies focuses on the LP problem with a homogeneous structure. On the contrary, real social networks include some heterogeneous interactions. Also, in the LP problem, the learning process must move on to the next stage with some particular order at the same time as convergence speed increases with no loss of accuracy. Therefore, there is a need for an algorithm that is compatible with heterogeneous social networks. This paper presents a new Continuous Action-Set Cellular Learning Automaton (CACLA), and we call it Multi-Wave CACLA (MWCACLA). MWCACLA has a connected structure, and there is a module of CALAs in each cell where a cell’s neighbors are its successors. We show that the model converges upon a stable and compatible configuration. Then we propose an MWCACLA model for considering the LP problem in a weighted multi-layer social network, and the proposed model is called MWCALA-WLP. The MWCALA-WLP merges the gained local information of different CALAs residents in a module related to a test link for estimating that the link will be appeared in the future or not. For the LP problem in the weighted multi-layer social networks, it has been demonstrated that MWCACLA produces much better results than other approaches and has an acceptable convergence rate.

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Data availability

The data that support the findings of this study are available in Higgs Twitter Dataset at https://snap.stanford.edu/data/higgs-twitter.html [54], and DBLP at https://www.aminer.org/data/ [55] which are used in our experiments.

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Authors and Affiliations

  1. Department of Computer Engineering, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran

    Mozhdeh Khaksar Manshad

  2. Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran

    Mohammad Reza Meybodi

  3. Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Afshin Salajegheh

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  1. Mozhdeh Khaksar Manshad
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  2. Mohammad Reza Meybodi
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  3. Afshin Salajegheh
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Correspondence to Mozhdeh Khaksar Manshad.

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Khaksar Manshad, M., Meybodi, M.R. & Salajegheh, A. A new multi-wave continuous action-set cellular learning automata for link prediction problem in weighted multi-layer social networks. J Supercomput 78, 18636–18665 (2022). https://doi.org/10.1007/s11227-022-04615-z

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