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Erjia Yan

Publications and source records attributed to Erjia Yan.

At least 19 recordsLinked to original sources

Global training and the collaborative structure of elite U.S. science

Globally trained scientific labor is a substantial component of U.S. universities, yet the organizational mechanisms linking foreign degree training to elite scientific output remain poorly understood. We link comprehensive U.S. faculty rosters to more than 12 million OpenAlex-indexed faculty-publication observations from 2011 to 2020. Faculty with non-U.S. degrees constitute one-tenth of the U.S. professoriate but account for larger shares of total publications and top-1% cited papers. This overrepresentation is concentrated in high-output disciplinary domains and research-intensive institutions. Within institution - domain - rank - year strata, however, differences in top-1% output, FWCI, and corresponding-author share attenuate sharply, indicating that much of the aggregate pattern reflects organizational placement rather than large within-context citation advantages. Collaboration structure further differentiates foreign- and domestically trained faculty: mixed domestic-foreign faculty teams exhibit substantially elevated elite-output rates, and the association attenuates strongly after accounting for team size, suggesting that collaboration scale is central to the pattern. Topic-distinctiveness analyses show little evidence that foreign-degree faculty occupy unusually rare research niches. Overall, foreign-degree training is best understood less as an individual productivity attribute than as a structural feature of elite U.S. science, operating through institutional concentration and collaborative integration.

cs.DL

Faculty mobility reallocates research capacity within persistent institutional hierarchies

Faculty mobility is often understood as a mechanism through which universities redistribute scientific talent and potentially improve research performance. Yet the system-level structure of mobility and its association with individual research trajectories have rarely been examined together. We link longitudinal faculty rosters from U.S. research universities to OpenAlex publication records and study 11,535 tenure-system faculty members who changed institutions between 2011 and 2020, with a comparison group of more than 200,000 non-moving faculty members. A directed network of faculty moves reveals a strongly hierarchical market: high-prestige institutions are net importers, lower-prestige institutions are net exporters, and the mobility hierarchy closely parallels the hierarchy observed in faculty hiring. However, event-study models that account for pre-move trajectories show little evidence of sustained post-move gains in publication volume, citation impact, or top-cited publication rates, including among upward moves to more prestigious institutions. The most consistent post-move change is collaborative: movers form new coauthor ties. We also observe modest increases in the share of papers with positive CD-index values. These patterns suggest that faculty mobility primarily reallocates existing research capacity within a persistent institutional hierarchy rather than systematically altering individual research trajectories.

cs.DL

AI-assisted writing and the reorganization of scientific knowledge

Generative AI systems such as ChatGPT are increasingly used in scientific writing, yet their broader implications for the organization of scientific knowledge remain unclear. We examine whether AI-assisted writing intensity, measured as the share of text in a paper that is predicted to exhibit features consistent with LLM-generated text, is associated with scientific disruption and knowledge recombination. Using approximately two million full-text research articles published between 2021 and 2024 and linked to citation networks, we document a sharp temporal pattern beginning in 2023. Before 2023, higher AI-assisted writing intensity is weakly or negatively associated with disruption; after 2023, the association becomes positive in within-author, within-field analyses. Over the same period, the positive association between AI-assisted writing intensity and cross-field citation breadth weakens substantially, and the negative association with citation concentration attenuates. Thus, the post-2023 increase in disruption is not accompanied by broader knowledge sourcing. These patterns suggest that generative AI is associated with more disruptive citation structures without a corresponding expansion in cross-field recombination. Rather than simply broadening the search space of science, AI-assisted writing may be associated with new forms of recombination built from relatively narrower knowledge inputs.

cs.DL

Citation Cascade and the Evolution of Topic Relevance

Citation analysis, as a tool for quantitative studies of science, has long emphasized direct citation relations, leaving indirect or high order citations overlooked. However, a series of early and recent studies demonstrate the existence of indirect and continuous citation impact across generations. Adding to the literature on high order citations, we introduce the concept of a citation cascade: the constitution of a series of subsequent citing events initiated by a certain publication. We investigate this citation structure by analyzing more than 450,000 articles and over 6 million citation relations. We show that citation impact exists not only within the three generations documented in prior research, but also in much further generations. Still, our experimental results indicate that two to four generations are generally adequate to trace a work's scientific impact. We also explore specific structural properties such as depth, width, structural virality, and size, which account for differences among individual citation cascades. Finally, we find evidence that it is more important for a scientific work to inspire trans domain (or indirectly related domain) works than to receive only intra domain recognition in order to achieve high impact. Our methods and findings can serve as a new tool for scientific evaluation and the modeling of scientific history.

cs.SI

Nine Million Book Items and Eleven Million Citations: A Study of Book-Based Scholarly Communication Using OpenCitations

Books have been widely used to share information and contribute to human knowledge. However, the quantitative use of books as a method of scholarly communication is relatively unexamined compared to journal articles and conference papers. This study uses the COCI dataset (a comprehensive open citation dataset provided by OpenCitations) to explore books' roles in scholarly communication. The COCI data we analyzed includes 445,826,118 citations from 46,534,705 bibliographic entities. By analyzing such a large amount of data, we provide a thorough, multifaceted understanding of books. Among the investigated factors are 1) temporal changes to book citations; 2) book citation distributions; 3) years to citation peak; 4) citation half-life; and 5) characteristics of the most-cited books. Results show that books have received less than 4% of total citations, and have been cited mainly by journal articles. Moreover, 97.96% of books have been cited fewer than ten times. Books take longer than other bibliographic materials to reach peak citation levels, yet are cited for the same duration as journal articles. Most-cited books tend to cover general (yet essential) topics, theories, and technological concepts in mathematics and statistics.

cs.DL

Correlated Anomaly Detection from Large Streaming Data

Correlated anomaly detection (CAD) from streaming data is a type of group anomaly detection and an essential task in useful real-time data mining applications like botnet detection, financial event detection, industrial process monitor, etc. The primary approach for this type of detection in previous researches is based on principal score (PS) of divided batches or sliding windows by computing top eigenvalues of the correlation matrix, e.g. the Lanczos algorithm. However, this paper brings up the phenomenon of principal score degeneration for large data set, and then mathematically and practically prove current PS-based methods are likely to fail for CAD on large-scale streaming data even if the number of correlated anomalies grows with the data size at a reasonable rate; in reality, anomalies tend to be the minority of the data, and this issue can be more serious. We propose a framework with two novel randomized algorithms rPS and gPS for better detection of correlated anomalies from large streaming data of various correlation strength. The experiment shows high and balanced recall and estimated accuracy of our framework for anomaly detection from a large server log data set and a U.S. stock daily price data set in comparison to direct principal score evaluation and some other recent group anomaly detection algorithms. Moreover, our techniques significantly improve the computation efficiency and scalability for principal score calculation.

cs.LG

Large-Scale Joint Topic, Sentiment & User Preference Analysis for Online Reviews

This paper presents a non-trivial reconstruction of a previous joint topic-sentiment-preference review model TSPRA with stick-breaking representation under the framework of variational inference (VI) and stochastic variational inference (SVI). TSPRA is a Gibbs Sampling based model that solves topics, word sentiments and user preferences altogether and has been shown to achieve good performance, but for large data set it can only learn from a relatively small sample. We develop the variational models vTSPRA and svTSPRA to improve the time use, and our new approach is capable of processing millions of reviews. We rebuild the generative process, improve the rating regression, solve and present the coordinate-ascent updates of variational parameters, and show the time complexity of each iteration is theoretically linear to the corpus size, and the experiments on Amazon data sets show it converges faster than TSPRA and attains better results given the same amount of time. In addition, we tune svTSPRA into an online algorithm ovTSPRA that can monitor oscillations of sentiment and preference overtime. Some interesting fluctuations are captured and possible explanations are provided. The results give strong visual evidence that user preference is better treated as an independent factor from sentiment.

cs.IR

Fast Botnet Detection From Streaming Logs Using Online Lanczos Method

Botnet, a group of coordinated bots, is becoming the main platform of malicious Internet activities like DDOS, click fraud, web scraping, spam/rumor distribution, etc. This paper focuses on design and experiment of a new approach for botnet detection from streaming web server logs, motivated by its wide applicability, real-time protection capability, ease of use and better security of sensitive data. Our algorithm is inspired by a Principal Component Analysis (PCA) to capture correlation in data, and we are first to recognize and adapt Lanczos method to improve the time complexity of PCA-based botnet detection from cubic to sub-cubic, which enables us to more accurately and sensitively detect botnets with sliding time windows rather than fixed time windows. We contribute a generalized online correlation matrix update formula, and a new termination condition for Lanczos iteration for our purpose based on error bound and non-decreasing eigenvalues of symmetric matrices. On our dataset of an ecommerce website logs, experiments show the time cost of Lanczos method with different time windows are consistently only 20% to 25% of PCA.

cs.LG

Challenges of measuring the impact of software: an examination of the lme4 R package

The rise of software as a research object is mirrored in the increasing interests towards quantitative studies of scientific software. However, due to the inconsistent practice of citing software, most of the existing studies analyzing the impact of scientific software are based on identification of software name mentions in full-text publications. Despite its limitations, citation data have a much larger quantity and broader coverage of scientific fields than full-text data and thus could support findings in much larger scopes. This paper presents an analysis aiming to evaluate the extent to which citations data can be used to reconstruct the impact of software. Specifically, we identified the variety of citable objects related to the lme4 R package and examined how the package's impact is scattered across these objects. Our results reveal a little-discussed challenge of using citation data to measure the impact of software, that even within the category of formal citation, there might be different forms in which the same software object is cited. This challenge can be mitigated by more carefully selecting objects as the proxy of software. However, it cannot be fully solved until we have one-software-one-proxy policy for software citation.

cs.DL

A natural language interface to a graph-based bibliographic information retrieval system

With the ever-increasing scientific literature, there is a need on a natural language interface to bibliographic information retrieval systems to retrieve related information effectively. In this paper, we propose a natural language interface, NLI-GIBIR, to a graph-based bibliographic information retrieval system. In designing NLI-GIBIR, we developed a novel framework that can be applicable to graph-based bibliographic information retrieval systems. Our framework integrates algorithms/heuristics for interpreting and analyzing natural language bibliographic queries. NLI-GIBIR allows users to search for a variety of bibliographic data through natural language. A series of text- and linguistic-based techniques are used to analyze and answer natural language queries, including tokenization, named entity recognition, and syntactic analysis. We find that our framework can effectively represents and addresses complex bibliographic information needs. Thus, the contributions of this paper are as follows: First, to our knowledge, it is the first attempt to propose a natural language interface to graph-based bibliographic information retrieval. Second, we propose a novel customized natural language processing framework that integrates a few original algorithms/heuristics for interpreting and analyzing natural language bibliographic queries. Third, we show that the proposed framework and natural language interface provide a practical solution in building real-world natural language interface-based bibliographic information retrieval systems. Our experimental results show that the presented system can correctly answer 39 out of 40 example natural language queries with varying lengths and complexities.

cs.IR

Finding knowledge paths among scientific disciplines

This paper discovers patterns of knowledge dissemination among scientific disciplines. While the transfer of knowledge is largely unobservable, citations from one discipline to another have been proven to be an effective proxy to study disciplinary knowledge flow. This study constructs a knowledge flow network in that a node represents a Journal Citation Report subject category and a link denotes the citations from one subject category to another. Using the concept of shortest path, several quantitative measurements are proposed and applied to a knowledge flow network. Based on an examination of subject categories in Journal Citation Report, this paper finds that social science domains tend to be more self-contained and thus it is more difficult for knowledge from other domains to flow into them; at the same time, knowledge from science domains, such as biomedicine-, chemistry-, and physics-related domains can access and be accessed by other domains more easily. This paper also finds that social science domains are more disunified than science domains, as three fifths of the knowledge paths from one social science domain to another need at least one science domain to serve as an intermediate. This paper contributes to discussions on disciplinarity and interdisciplinarity by providing empirical analysis.

cs.DL

Entitymetrics: Measuring the Impact of Entities

This paper proposes entitymetrics to measure the impact of knowledge units. Entitymetrics highlight the importance of entities embedded in scientific literature for further knowledge discovery. In this paper, we use Metformin, a drug for diabetes, as an example to form an entity-entity citation network based on literature related to Metformin. We then calculate the network features and compare the centrality ranks of biological entities with results from Comparative Toxicogenomics Database (CTD). The comparison demonstrates the usefulness of entitymetrics to detect most of the outstanding interactions manually curated in CTD.

cs.DL

A bird's-eye view of scientific trading: Dependency relations among fields of science

We use a trading metaphor to study knowledge transfer in the sciences as well as the social sciences. The metaphor comprises four dimensions: (a) Discipline Self-dependence, (b) Knowledge Exports/Imports, (c) Scientific Trading Dynamics, and (d) Scientific Trading Impact. This framework is applied to a dataset of 221 Web of Science subject categories. We find that: (i) the Scientific Trading Impact and Dynamics of Materials Science And Transportation Science have increased; (ii) Biomedical Disciplines, Physics, And Mathematics are significant knowledge exporters, as is Statistics & Probability; (iii) in the social sciences, Economics, Business, Psychology, Management, And Sociology are important knowledge exporters; (iv) Discipline Self-dependence is associated with specialized domains which have ties to professional practice (e.g., Law, Ophthalmology, Dentistry, Oral Surgery & Medicine, Psychology, Psychoanalysis, Veterinary Sciences, And Nursing).

cs.DL

A recursive field-normalized bibliometric performance indicator: An application to the field of library and information science

Two commonly used ideas in the development of citation-based research performance indicators are the idea of normalizing citation counts based on a field classification scheme and the idea of recursive citation weighing (like in PageRank-inspired indicators). We combine these two ideas in a single indicator, referred to as the recursive mean normalized citation score indicator, and we study the validity of this indicator. Our empirical analysis shows that the proposed indicator is highly sensitive to the field classification scheme that is used. The indicator also has a strong tendency to reinforce biases caused by the classification scheme. Based on these observations, we advise against the use of indicators in which the idea of normalization based on a field classification scheme and the idea of recursive citation weighing are combined.

cs.DL

Applying centrality measures to impact analysis: A coauthorship network analysis

Many studies on coauthorship networks focus on network topology and network statistical mechanics. This article takes a different approach by studying micro-level network properties, with the aim to apply centrality measures to impact analysis. Using coauthorship data from 16 journals in the field of library and information science (LIS) with a time span of twenty years (1988-2007), we construct an evolving coauthorship network and calculate four centrality measures (closeness, betweenness, degree and PageRank) for authors in this network. We find out that the four centrality measures are significantly correlated with citation counts. We also discuss the usability of centrality measures in author ranking, and suggest that centrality measures can be useful indicators for impact analysis.

cs.DL

Discovering author impact: A PageRank perspective

This article provides an alternative perspective for measuring author impact by applying PageRank algorithm to a coauthorship network. A weighted PageRank algorithm considering citation and coauthorship network topology is proposed. We test this algorithm under different damping factors by evaluating author impact in the informetrics research community. In addition, we also compare this weighted PageRank with the h-index, citation, and program committee (PC) membership of the International Society for Scientometrics and Informetrics (ISSI) conferences. Findings show that this weighted PageRank algorithm provides reliable results in measuring author impact.

cs.DL

PageRank for ranking authors in co-citation networks

Google's PageRank has created a new synergy to information retrieval for a better ranking of Web pages. It ranks documents depending on the topology of the graphs and the weights of the nodes. PageRank has significantly advanced the field of information retrieval and keeps Google ahead of competitors in the search engine market. It has been deployed in bibliometrics to evaluate research impact, yet few of these studies focus on the important impact of the damping factor (d) for ranking purposes. This paper studies how varied damping factors in the PageRank algorithm can provide additional insight into the ranking of authors in an author co-citation network. Furthermore, we propose weighted PageRank algorithms. We select 108 most highly cited authors in the information retrieval (IR) area from the 1970s to 2008 to form the author co-citation network. We calculate the ranks of these 108 authors based on PageRank with damping factor ranging from 0.05 to 0.95. In order to test the relationship between these different measures, we compare PageRank and weighted PageRank results with the citation ranking, h-index, and centrality measures. We found that in our author co-citation network, citation rank is highly correlated with PageRank's with different damping factors and also with different PageRank algorithms; citation rank and PageRank are not significantly correlated with centrality measures; and h-index is not significantly correlated with centrality measures.

cs.DL

Upper Tag Ontology (UTO) For Integrating Social Tagging Data

Data integration and mediation have become central concerns of information technology over the past few decades. With the advent of the Web and the rapid increases in the amount of data and the number of Web documents and users, researchers have focused on enhancing the interoperability of data through the development of metadata schemes. Other researchers have looked to the wealth of metadata generated by bookmarking sites on the Social Web. While several existing ontologies capitalize on the semantics of metadata created by tagging activities, the Upper Tag Ontology (UTO) emphasizes the structure of tagging activities to facilitate modeling of tagging data and the integration of data from different bookmarking sites as well as the alignment of tagging ontologies. UTO is described and its utility in harvesting, modeling, integrating, searching and analyzing data is demonstrated with metadata harvested from three major social tagging systems (Delicious, Flickr and YouTube).

cs.IR