The effect of social media knowledge cascade: an analysis of scientific papers diffusion

Hou, Jianhua; Yang, Xiucai; Zhang, Yang

doi:10.1007/s11192-023-04785-4

Cited by 2 publications

(2 citation statements)

References 84 publications

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“…Among them, citation cascades have become a prominent one in recent years [5,6,35]. It is utilized for various tasks such as technology forecasting [2], agent knowledge structure [36] or intelligence structure [37], impact evaluation [38], scientific evaluation [39], topic detection or hot spot prediction [6], and cascade information exploration in social media [40]. A typical example of citation cascades is illustrated in Figure 2B.…”

Section: Citation Cascadesmentioning

confidence: 99%

Predicting Scientific Breakthroughs Based on Structural Dynamic of Citation Cascades

Yu,

Liang,

Xie

2024

Mathematics

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Predicting breakthrough papers holds great significance; however, prior studies encountered challenges in this task, indicating a need for substantial improvement. We propose that the failure to capture the dynamic structural-evolutionary features of citation networks is one of the major reasons. To overcome this limitation, this paper introduces a new method for constructing citation cascades of focus papers, allowing the creation of a time-series-like set of citation cascades. Then, through a thorough review, three types of structural indicators in these citation networks that could reflect breakthroughs are identified, including certain basic topological metrics, PageRank values, and the von Neumann graph entropy. Based on the time-series-like set of citation cascades, the dynamic trajectories of these indicators are calculated and employed as predictors. Using the Nobel Prize-winning papers as a landmark dataset, our prediction method yields approximately a 7% improvement in the ROC-AUC score compared to static-based prior methods. Additionally, our method advances in achieving earlier predictions than other previous methods. The main contribution of this paper is proposing a novel method for creating citation cascades in chronological order and confirming the significance of predicting breakthroughs from a dynamic structural perspective.

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Section: Citation Cascadesmentioning

confidence: 99%

Predicting Scientific Breakthroughs Based on Structural Dynamic of Citation Cascades

Yu,

Liang,

Xie

2024

Mathematics

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“…Knowledge acquisition represents the process by which an enterprise or industry absorbs and integrates new knowledge and technology from the external environment. It plays a critical role in promoting innovation, improving performance, and driving industry collaborative innovation [47,48], playing a critical role in fostering innovation, enhancing performance, and driving industry collaborative innovation [49]. Existing research categorizes knowledge acquisition into two dimensions: breadth and depth of knowledge acquisition [50,51].…”

Section: Knowledge Acquisition As a Mediating Variablementioning

confidence: 99%

Research on the Impact of Inter-Industry Innovation Networks on Collaborative Innovation Performance: A Case Study of Strategic Emerging Industries

Shi,

Xiao

2024

Systems

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As global economic competition intensifies, collaborative innovation in strategic emerging industries has become a key factor in promoting economic growth and business development, attracting widespread attention. To delve into the mechanisms of collaborative innovation among China’s strategic emerging industries, this study employs a social network analysis. It investigates the characteristics of these industries by analyzing 8,775,496 invention patents, exploring the impact of inter-industry innovation networks on collaborative innovation. Furthermore, this study incorporates industry knowledge acquisition as a mediating variable and the network density within individual industries as a moderating variable, to more comprehensively explain this impact mechanism. We find that relational and structural embeddings within inter-industry innovation networks significantly positively affect collaborative innovation performance, highlighting the importance of partnerships and the critical role of network configurations. Additionally, the breadth and depth of knowledge acquisition mediate the relationship between relational embedding in inter-industry innovation networks and collaborative innovation performance, emphasizing the pivotal role of knowledge acquisition in fostering collaborative innovation. Lastly, the network density within individual industries positively moderates the relationships between both relational and structural embedding in inter-industry innovation networks and collaborative innovation performance, revealing specific impacts of both internal and external industry innovation network characteristics on collaborative innovation. These findings not only provide practical guidance for collaborative innovation across industries but also offer new insights and implications for policy-making and academic research. In future industrial development, special emphasis should be placed on establishing and maintaining partnerships, optimizing inter-industry innovation networks, and enhancing the breadth and depth of knowledge acquisition to foster sustainable development of collaborative innovation. This is crucial for enhancing corporate competitiveness, creating more job opportunities, and driving innovative economic development.

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The effect of social media knowledge cascade: an analysis of scientific papers diffusion

Cited by 2 publications

References 84 publications

Predicting Scientific Breakthroughs Based on Structural Dynamic of Citation Cascades

Predicting Scientific Breakthroughs Based on Structural Dynamic of Citation Cascades

Research on the Impact of Inter-Industry Innovation Networks on Collaborative Innovation Performance: A Case Study of Strategic Emerging Industries

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