TY - GEN
T1 - TVGN: Mastering Predictions of Information Transmissibility in Time-Varying Networks
AU - Shi, Xinrui
AU - Li, Yupeng
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025/6/6
Y1 - 2025/6/6
N2 - In the era of information overload, various types of information interconnect to form complex networks. To better manage the diffusion paths and ensure that relevant information reaches the appropriate audiences while curbing the spread of less useful content, we propose to predict the transmissibility-the probability of each piece of information being transmitted influenced by other information within the network, which can be applied in recommendation systems and advertising placement. Diverging from existing research that focus on static networks only, our work addresses the more realistic scenario of time-varying networks where the influence strength between related information evolves over time. We propose a novel graph neural network model, Temporal Variation Graph Network (TVGN), to predict the transmissibility by capturing time-varying features and learn the patterns of dynamic diffusion processes. Our proposed model is evaluated on two popular citation networks, Cora [10] and CiteSeer [13]. Our results demonstrate that our model achieves low estimation error, outperforming state-of-the-art models.
AB - In the era of information overload, various types of information interconnect to form complex networks. To better manage the diffusion paths and ensure that relevant information reaches the appropriate audiences while curbing the spread of less useful content, we propose to predict the transmissibility-the probability of each piece of information being transmitted influenced by other information within the network, which can be applied in recommendation systems and advertising placement. Diverging from existing research that focus on static networks only, our work addresses the more realistic scenario of time-varying networks where the influence strength between related information evolves over time. We propose a novel graph neural network model, Temporal Variation Graph Network (TVGN), to predict the transmissibility by capturing time-varying features and learn the patterns of dynamic diffusion processes. Our proposed model is evaluated on two popular citation networks, Cora [10] and CiteSeer [13]. Our results demonstrate that our model achieves low estimation error, outperforming state-of-the-art models.
KW - Graph neural network
KW - Information network
KW - Transmissibility
UR - http://www.scopus.com/inward/record.url?scp=105008664515&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-6389-7_13
DO - 10.1007/978-981-96-6389-7_13
M3 - Conference proceeding
SN - 9789819663880
T3 - Lecture Notes in Computer Science
SP - 148
EP - 160
BT - Computational Data and Social Networks
A2 - Kertesz, Janos
A2 - Li, Bo
A2 - Supnithi, Thepchai
A2 - Takhom, Akkharawoot
PB - Springer
T2 - The 13th International Conference on Computational Data and Social Networks, CSoNet 2024
Y2 - 16 December 2024 through 18 December 2024
ER -