A meta-learning based approach for temporal link prediction in multiplex networks

Link prediction in temporal and multiplex networks is a crucial issue across both applied and scientific disciplines within the study of complex networks. Recent advances in hardware and the increased availability of computational resources have enhanced our capacity to tackle this problem more effectively. Link prediction in multiplex and temporal networks faces challenges such as inter-layer dependencies and the temporal expansion and contraction of the network. This paper introduces MetaLink, a novel approach designed for link prediction within such temporal multiplex networks. MetaLink leverages knowledge obtained from various temporal network snapshots by employing two innovative methods for subsequent temporal snapshot. It efficiently facilitates the transfer of knowledge across different temporal snapshots. The intra-layer knowledge transfer is governed by a time-decay function, while inter-layer knowledge is learned in a step wise and transferred using the Model-Agnostic Meta-Learning (MAML) algorithm from one snapshot (task) to another. Our findings demonstrate that MetaLink significantly outperforms static single-layer and multiplex methods, showing improvements of 2 to 5 percent, and exhibits up to a 3 percent enhancement over existing temporal methods.