LTVGN: Mastering Predictions of Information Transmissibility in Time-Varying Information Networks

Xinrui Shi; Yupeng Li

doi:10.53941/tai.2026.100001

Abstract

In the era of information overload, various types of information interconnect to form complex networks. To better manage diffusion paths within networks, we propose predicting information transmissibility—the probability of information being transmitted under the influence of other information in the network. Accurate transmissibility prediction has practical applications in recommendation systems and misinformation control, enabling relevant information to reach appropriate audiences while curbing the spread of less useful content. Given the characteristics of information networks, text-attributed graphs provide a natural representation that captures both network structure and content semantics. However, existing text-attributed graph representation methods fail to capture diffusion dynamics and incur high computational costs. Therefore, we propose a novel efficient textual-graph model, Language Temporal Variation Graph Network(LTVGN), to predict transmissibility by capturing time-varying features, structural information and textual information. Our proposed model is evaluated on the citation dataset HEP-TH. The results demonstrate that our model outperforms state-of-the-art models, achieving a low estimation error.

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