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

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.

Keywords

information network / transmissibility / text-attributed graph

Author Contributions

X.S.: conceptualization, data curation, original draft, reviewing and editing; Y.L.: conceptualization, supervision, reviewing and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by National Natural Science Foundation of China (No. 62202402), Guangdong and Hong Kong Universities “1 + 1 + 1” Joint Research Collaboration Scheme, Project No. 2025A0505000001, the Initiation Grant for Faculty Niche Research Areas 2023/24 (No. RC-FNRA-IG/23-24/COMM/01), the Early Career Scheme (ECS) from the Research Grants Council of HKSAR (HKBU 22202423), the General Research Fund (GRF) from the Research Grants Council of HKSAR (HKBU 12203425), and Germany/Hong Kong Joint Research Scheme sponsored by the Research Grants Council of Hong Kong and the German Academic Exchange Service of Germany (No. G-HKBU203/22).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No data is being made available.

Conflicts of Interest

The authors declare no conflict of interest.

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