Short-video Propagation Influence Rating: A New Real-world Dataset and A New Large Graph Model

• URL: http://arxiv.org/abs/2503.23746v1
• Date: Mon, 31 Mar 2025 05:53:15 GMT
• Title: Short-video Propagation Influence Rating: A New Real-world Dataset and A New Large Graph Model
• Authors: Dizhan Xue, Jing Cui, Shengsheng Qian, Chuanrui Hu, Changsheng Xu,
• Abstract summary: Cross-platform Short-Video dataset includes 117,720 videos, 381,926 samples, and 535 topics across 5 biggest Chinese platforms.<n>Large Graph Model (LGM) named NetGPT can bridge heterogeneous graph-structured data with the powerful reasoning ability and knowledge of Large Language Models (LLMs)<n>Our NetGPT can comprehend and analyze the short-video propagation graph, enabling it to predict the long-term propagation influence of short-videos.
• Score: 55.58701436630489
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Short-video platforms have gained immense popularity, captivating the interest of millions, if not billions, of users globally. Recently, researchers have highlighted the significance of analyzing the propagation of short-videos, which typically involves discovering commercial values, public opinions, user behaviors, etc. This paper proposes a new Short-video Propagation Influence Rating (SPIR) task and aims to promote SPIR from both the dataset and method perspectives. First, we propose a new Cross-platform Short-Video (XS-Video) dataset, which aims to provide a large-scale and real-world short-video propagation network across various platforms to facilitate the research on short-video propagation. Our XS-Video dataset includes 117,720 videos, 381,926 samples, and 535 topics across 5 biggest Chinese platforms, annotated with the propagation influence from level 0 to 9. To the best of our knowledge, this is the first large-scale short-video dataset that contains cross-platform data or provides all of the views, likes, shares, collects, fans, comments, and comment content. Second, we propose a Large Graph Model (LGM) named NetGPT, based on a novel three-stage training mechanism, to bridge heterogeneous graph-structured data with the powerful reasoning ability and knowledge of Large Language Models (LLMs). Our NetGPT can comprehend and analyze the short-video propagation graph, enabling it to predict the long-term propagation influence of short-videos. Comprehensive experimental results evaluated by both classification and regression metrics on our XS-Video dataset indicate the superiority of our method for SPIR.

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