Dynamic Link and Flow Prediction in Bank Transfer Networks

Shu Takahashi, Kento Yamamoto, Shumpei Kobayashi, Ryoma Kondo and Ryohei Hisano

Papers from arXiv.org

Abstract: The prediction of both the existence and weight of network links at future time points is essential as complex networks evolve over time. Traditional methods, such as vector autoregression and factor models, have been applied to small, dense networks, but become computationally impractical for large-scale, sparse, and complex networks. Some machine learning models address dynamic link prediction, but few address the simultaneous prediction of both link presence and weight. Therefore, we introduce a novel model that dynamically predicts link presence and weight by dividing the task into two sub-tasks: predicting remittance ratios and forecasting the total remittance volume. We use a self-attention mechanism that combines temporal-topological neighborhood features to predict remittance ratios and use a separate model to forecast the total remittance volume. We achieve the final prediction by multiplying the outputs of these models. We validated our approach using two real-world datasets: a cryptocurrency network and bank transfer network.

Date: 2024-09, Revised 2024-10
New Economics Papers: this item is included in nep-ban, nep-big, nep-ipr, nep-net and nep-pay
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Published in Complex Networks & Their Applications XIII: Proceedings of the 13th International Conference on Complex Networks and Their Applications (COMPLEX NETWORKS 2024)

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