Scientometrics最新文献

Author networks in science often rely on citation analyses. In such cases, as in others, network interpretation usually depends on supplementary data, notably about authors’ research domains when disciplinary interpretations are sought. More general social networks also face similar interpretation challenges as to the semantic content specificities of their members. In this research-in-progress, we propose to infer author networks not from citation analyses but from topic similarity analyses based on a topic-model of published documents. Such author networks reveal, as we call them, “hidden communities of interest” (HCoIs) whose semantic content can easily be interpreted by means of their associated topics in the model. We use an astrobiology corpus of full-text articles (N = 3,698) to illustrate the approach. Having conducted an LDA topic-model on all publications, we identify the underlying communities of authors by measuring author correlations in terms of topic distributions. Adding publication dates makes it possible to examine HCoI evolution over time. This approach to social networks supplements traditional methods in contexts where textual data are available.

Industrial clusters, geographical concentrations of interconnected companies, aim to achieve technological innovation by acquiring common technology, which is the technology shared by all companies in an industrial cluster. Obtaining patents from universities is a primary way to gain common technology. However, existing patent recommendation methods have primarily focused on meeting the technological needs of individual companies, thus falling short in addressing the common technological requirements of all companies within an industrial cluster. To address the problem, we propose a deep learning (DL) method that recommends patents to industrial clusters based on common technological needs mining (DL_CTNM). The proposed method mines the common needs from patents owned by the companies and domain knowledge about potential technologies common to industries. Specifically, we mine the technological needs of the companies from their patents using long short-term memory networks and obtain their patent-based common needs by designing a candidate patent-aware attention mechanism. Then, we extract implicit technology directions from the domain knowledge using a capsule network and obtain domain knowledge-based common needs by designing an industrial cluster-aware attention mechanism. We evaluate the proposed method through offline and online experiments, comparing it to various benchmark methods. The experimental results demonstrate that our method outperforms these benchmarks in terms of recall and normalized discounted cumulative gain.

The purpose of this paper is to consider the joint action of structural, cognitive and relational social capital, and to explore the formation mechanism of the innovation network of new energy vehicles (NEV). The research data come from China's NEV cooperative invention patent applications from 2001 to 2019. This paper uses the exponential random graph model (ERGM) to study the impact of different dimensions of social capital on the NEV industry-university-research (I-U-R) innovation network. The results show that from the perspective of structural capital, the closed network structure has a positive impact on the formation of NEV I-U-R innovation network. From the perspective of cognitive capital, the homogeneity of knowledge base has a positive effect on the formation of the NEV I-U-R innovation network, and the innovation subjects with the same knowledge base breadth and the same knowledge base depth are more inclined to form a cooperative relationship. For relational capital, institutional environment similarity and organizational structure similarity are important factors affecting the formation of NEV I-U-R innovation network to a similar extent. The findings of this study provide scientific references for promoting the sustainable development of I-U-R innovation network in NEV industry.

Technology fusion refers to the phenomenon in which distinct technology domains overlap. Despite its importance in technology innovation and evolution, few studies have explored the general pattern of the cross-domain search process leading to technology fusion. This paper proposes that the stretching between distinct technology domains could be viewed as searching in a two-dimensional knowledge partition landscape and then empirically validates the model based on a large patent dataset derived from the U.S. Patent and Trade Office (USPTO). The findings show that the general pattern of the search processes leading to technology fusion could be viewed as searching across a broad technology scope to identify limited valuable linking points within existing technology domains, and the search processes are mainly “divergent”; that is, innovative agents gradually extend the search scope to pursue new hybrid technologies. However, the cross-domain search would be more targeted if the two technology domains were closer to each other. In addition, compared to searching across a broader technology scope, digging in certain technology areas is more important for the generation of new high-impact hybrid technologies. This study provides a novel perspective for understanding the new knowledge creation process and technology fusion.

Academic collaboration is fundamental to the advancement of scientific research. However, with the growing number of publications and researchers, it becomes increasingly challenging to identify suitable collaborators. Academic collaborator recommendation is a promising solution to this problem. Traditional recommendation methods based on collaborative filtering suffer serious data sparsity. In recent years, network topology-based methods have shown good recommendation performance while alleviating the data sparsity issue to some extent by exploiting the relationships between nodes and their attributes. Nevertheless, these methods are typically based on homogeneous collaboration networks that consist only of scholar nodes and collaboration relationships, leading to suboptimal performance. In reality, collaboration involves many different types of nodes and relations that accumulate multiplex information. To address this issue, we construct a heterogeneous academic information network comprising four types of nodes: scholars, papers, organizations, and publication venues. An academic collaborator recommendation model is designed to capture multi-type attribute features and network topology features of nodes through metapaths based on the network. Specifically, the attribute features of nodes are embedded by a node type-aware embedding method. The topology features are then extracted through the node type-aware aggregation and metapath instance aggregation procedure. After that, we utilize a metapath aggregation method to gather different types of metapaths, each representing a factor that affects collaboration. Thus, the topology information and attribute information are preserved, while encompassing multi-type factors of collaboration. Finally, we compute the vector similarity to determine collaborators. Through rigorous experimentation on a large-scale interdisciplinary academic dataset, we found that the proposed model exhibits outstanding performance in practical applications. Unlike traditional approaches confined to homogeneous collaboration networks, our model delves deeper by mining and leveraging diverse node attributes and multiple collaboration influencing factors. This approach significantly enhances the accuracy and effectiveness of collaborator recommendations. Ultimately, we aspire to contribute to a more efficient and accessible platform that simplifies the search for suitable collaborators.

As a phased achievement of technological innovation, patent analysis holds extraordinary research significance. By constructing patent citation networks, scholars have proposed various centrality algorithms (such as citation count, PageRank, LeaderRank, etc.) for evaluating the quality and influence of patents. However, these centrality algorithms suffer from age bias, which means these algorithms are more inclined to obtain higher rankings for older patents, thus losing fairness to younger patents. Additionally, the selection of algorithm performance evaluation indicators is crucial. If the indicators are not chosen appropriately, the results may be affected. Therefore, based on the background of Chinese green patents, this paper develops an unbiased evaluation ranking algorithm to identify significant Chinese green patents earlier. The results demonstrate that the combination of the rescaled method and the AttriRank algorithm can effectively obtain the importance of patents, and provide a systematic and reasonable evaluation method for measuring patent value.

Scientific uncertainty is an integral part of the research process and inherent to the construction of new knowledge. In this paper, we investigate the ways in which uncertainty is expressed in articles and propose a new interdisciplinary annotation framework to categorize sentences containing uncertainty expressions along five dimensions. We propose a method for the automatic annotation of sentences based on linguistic patterns for identifying the expressions of scientific uncertainty that have been derived from a corpus study. We processed a corpus of 5956 articles from 22 journals in three different discipline groups, which were annotated using our automatic annotation method. We evaluate our annotation method and study the distribution of uncertainty expressions across the different journals and categories. The results show a predominant concentration of the distribution of the scientific uncertainty expressions in the Results and Discussion section (71.4%), followed by 12.5% of expressions in the Background section, and the largest proportion of uncertainty expressions, approximately 70.3%, are formed as author(s) statements. Our research contributes methodological advances and insights into the diverse manifestations of scientific uncertainty across disciplinary domains and provides a basis for ongoing exploration and refinement of the understanding of scientific uncertainty communication.

Scientific experience is crucial for producing high-quality research, and the approach of acquisition can significantly impact its accumulation rate. We present a framework for scientific experience acquisition that outlines the dual dimensions of experience accumulation: self-accumulation and accumulation under senior expert guidance. To validate the framework, we conducted a case study using 2,957,700 papers from all 568 Chinese humanities and social science journals, taking into account the limitations of the international journal system. Our findings reveal that self-accumulation has been gradually declining, decreasing from 57.67% in 1980 to 4.55% in 2020. Conversely, accumulation under senior expert guidance has been steadily increasing, rising from 5.7% in 1980 to 28.69% in 2020. Furthermore, the proportion of the two approaches varies by discipline. Social sciences such as Psychology, Economics, and Management, which rely more on large teams and collaborative research, have a higher proportion of accumulation under senior expert guidance than humanities disciplines like Art, History, and Philosophy, which depend more on individual research. Finally, this research also offers a distinctive exploration of the question posed by the US National Science and Technology Council (2008): how and why do communities of innovation form and evolve.

Understanding knowledge transfer patterns is essential in providing valuable insights for shaping innovations and supporting economic growth. Our study identifies the main contributors and patterns of knowledge transfer within the pharmacology field from 2000 to 2019 by analyzing citation linkage and collaborative information between sector categories, affiliated institutions, and biomedical entities in articles from the Web of Science database. Our main contribution is mapping the knowledge transfer flow and identifying the main contributors to knowledge transfer within the pharmacology domain. We manually categorized affiliated institutions into four sector categories to observe knowledge transfer patterns. Subsequently, we performed a citation linkage analysis at three levels: sector categories, institution names, and biomedical entities. The results show that academic institutions are the most significant contributors to knowledge transfer in the pharmacology field, followed by commercial and government institutions. Although the majority of knowledge transfers originated from academic institutions, our study uncovered notable transfers from commercial to academic sectors and from government to academic sectors. Through named entity analysis on diseases, drugs, and genes, we found that research in the pharmacology field predominantly concentrates on subjects pertaining to cancers, chronic diseases, and neurodegenerative disorders.

Academic collaboration can break through the geographical limitations of scholars and promote academic output among scholars. Academic big data will provide an important data source for more comprehensive and accurate modeling scholars due to the coexistence environment of various academic entities. Based on academic big data, an end-to-end model HCSP was proposed for predicting collaborative relationships among scholars. HCSP models scholars from two aspects: content-based features and structure-based features, and calculate the similarity between scholars based on this to predict whether there will be academic collaboration between scholars. When learning the content-based features of scholars, HCSP utilizes LSTM and multi-head attention mechanism to extract the overall and recent research interests of scholars, to capture the diversity of scholars’ research interests. When learning the structure-based features of scholars, HCSP adopts a subgraph sampling strategy based on meta paths to model the structural features of scholar nodes in heterogeneous academic network. By integrating scholars’ content-based and structure-based features, HCSP calculates the similarity between scholars to determine whether there will be a collaborative relationship between them. The experimental results indicate that the HCSP model achieves better prediction performance compared to the baseline models. It can be seen that integrating scholars’ content-based and structure-based characteristics can indeed provide a richer and more effective modeling basis for predicting their academic collaborative relationships.