In supervised machine learning for author name disambiguation, negative training data are often dominantly larger than positive training data This paper examines how the ratios of negative to positive training data can affect the performance of machine learning algorithms to disambiguate author names in bibliographic records On multiple labeled datasets, three classifiers—Logistic Regression, Naive Bayes, and Random Forest—are trained through representative features such as coauthor names, and title words extracted from the same training data but with various positive-to-negative training data ratios Results show that increasing negative training data can improve disambiguation performance but with a few percent of performance gains and sometimes degrade it Logistic and Naive Bayes learn optimal disambiguation models even with a base ratio (1:1) of positive and negative training data Also, the performance improvement by Random Forest tends to quickly saturate roughly after 1:10~1:15 These findings imply that contrary to the common practice using all training data, name disambiguation algorithms can be trained using part of negative training data without degrading much disambiguation performance while increasing computational efficiency This study calls for more attention from author name disambiguation scholars to methods for machine learning from imbalanced data

The impact of imbalanced training data on machine learning for author name disambiguation

https://dspacecris.eurocris.org/bitstream/11366/683/1/Kosyakov_Guskov_CRIS2018_paper_Impact_national_science_policy.pdf

Impact of national science policy on academic migration and research productivity in Russia

This paper presents the results of the analysis of the causes for duplicate profiles in the Scopus database on the basis of a random sampling of profiles of 400 Russian authors and 400 organizations. We estimate the number of duplicate profiles and calculate the level of uncertainty that errors in bibliographic descriptions can contribute to the results of scientometric studies using the Scopus database. The analysis showed that in Scopus 76% of the organizations and 24% of the authors have duplicate profiles. In this regard, organizations lose an average of 17% of publications and authors lose 11%. The results of this study can be used in elaboration of the Scopus database and estimating the error level in the research assessment of institutions and individuals.

The Impact of Errors in the Sсopus Database on the Research Assessment

To train algorithms for supervised author name disambiguation, many studies have relied on hand-labeled truth data that are very laborious to generate. This paper shows that labeled data can be automatically generated using information features such as email address, coauthor names, and cited references that are available from publication records. For this purpose, high-precision rules for matching name instances on each feature are decided using an external-authority database. Then, selected name instances in target ambiguous data go through the process of pairwise matching based on the rules. Next, they are merged into clusters by a generic entity resolution algorithm. The clustering procedure is repeated over other features until further merging is impossible. Tested on 26 K instances out of the population of 228 K author name instances, this iterative clustering produced accurately labeled data with pairwise F1 = 0.99. The labeled data represented the population data in terms of name ethnicity and co-disambiguating name group size distributions. In addition, trained on the labeled data, machine learning algorithms disambiguated 24 K names in test data with performance of pairwise F1 = 0.90–0.92. Several challenges are discussed for applying this method to resolving author name ambiguity in large-scale scholarly data.

pdf/generating-automatically-labeled-data-for-author-name-4obes0gdem.pdf

Generating automatically labeled data for author name disambiguation: an iterative clustering method

Most network studies rely on a measured network that differs from the underlying network which is obfuscated by measurement errors. It is well known that such errors can have a severe impact on the reliability of network metrics, especially on centrality measures: a more central node in the observed network might be less central in the underlying network. Previous studies have dealt either with the general effects of measurement errors on centrality measures or with the treatment of erroneous network data. In this paper, we propose a method for estimating the impact of measurement errors on the reliability of a centrality measure, given the measured network and assumptions about the type and intensity of the measurement error. This method allows researchers to estimate the robustness of a centrality measure in a specific network and can, therefore, be used as a basis for decision-making. In our experiments, we apply this method to random graphs and real-world networks. We observe that our estimation is, in the vast majority of cases, a good approximation for the robustness of centrality measures. Beyond this, we propose a heuristic to decide whether the estimation procedure should be used. We analyze, for certain networks, why the eigenvector centrality is less robust than, among others, the pagerank. Finally, we give recommendations on how our findings can be applied to future network studies.

/pdf/influence-of-measurement-errors-on-networks-estimating-the-123ab360ih.pdf

Influence of measurement errors on networks: Estimating the robustness of centrality measures

Abstract Inequality perceptions differ along racial and gendered lines. To explain these disparities, we propose an agent-based model of localised perceptions of the gender and racial wage gap in networks. We show that the combination of homophilic graph formation and estimation based on locally limited knowledge can replicate both the underestimation of the gender or racial wage gap that empirical studies find and the well-documented fact that the underprivileged perceive the wage gap to be higher on average and with less bias. Similarly, we demonstrate that the underprivileged perceive overall inequality to be higher on average. In contrast to this qualitative replication, we also show that the effect of homophilic graph formation is quantitatively too strong to account for the empirically observed effect sizes within a recent Israeli sample on perceived gender wage gaps. As a parsimonious extension, we let agents estimate using a composite signal based on local and global information. Our calibration suggests that women place much more weight on the (correct) global signal than men, in line with psychological evidence that people adversely affected by group-based inequities pay more attention to global information about the issue. Our findings suggest that (educational) interventions about the global state of gender equality are much more likely to succeed than information treatments about overall inequality and that these interventions should target the privileged. 

/pdf/perception-and-privilege-3glmekju.pdf

Perception and privilege

/pdf/perception-and-privilege-1aqr4ceb.pdf

Bibliometric analyses depend on the quality of data sets and the author name disambiguation process (ANDP), which attributes author names on papers to real persons. Errors in a data set or the ANDP result in wrongly attributed papers to the wrong person. These errors can potentially distort the results of analyses based on such data sets. However, the general impact of data set quality on bibliometric analysis is mostly unknown; as such, an assessment is costly due to the manual steps involved. This paper presents an overview of the data set qualities produced by different ANDPs and uses simulations to study the general impact of data set quality on different bibliometric analysis (author rankings and regressions analysis with number of papers as dependent variable). The results show that rankings of authors are only valid on high quality data sets, which are typically not found directly in commercially available datasets. Both mean and individual per person data set quality is important for valid ranking results. Regressions are not as influenced by the overall data set quality but instead by individual quality differences between authors. Different types of errors can potentially bias the regression results. The outcome of this study also shows the importance of reporting both overall and individual variation in data set quality, so that the validity of analyses based on these data sets can be assessed.

Using Monte Carlo simulations to assess the impact of author name disambiguation quality on different bibliometric analyses

The nexus between debt and inequality has attracted considerable scholarly attention in the wake of the global ﬁnancial crisis. One prominent candidate to explain the striking co-evolution of income inequality and private debt in this period has been the theory of upward-looking consumption externalities leading to expenditure cascades. We propose a parsimonious model of upward-looking consumption at the micro level mediated by perception networks with empirically plausible topologies. This allows us to make sense of the ambiguous empirical literature on the relevance of this channel. Up to our knowledge, our approach is the ﬁrst to make the reference group to which conspicuous consumption relates explicit. Our model, based purely on current income, replicates the major stylised facts regarding micro consumption behaviour and is thus observationally equivalent to the workhorse permanent income hypothesis, without facing its dual problem of ‘excess smoothness’ and ‘excess sensitivity’. We also demonstrate that the network topology and segregation has a signiﬁcant eﬀect on consumption patterns which has so far been neglected.

https://www.econstor.eu/bitstream/10419/242826/1/1770907386.pdf

A Network Approach to Consumption

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Perception and privilege

Perception and privilege

Using Monte Carlo simulations to assess the impact of author name disambiguation quality on different bibliometric analyses

A Network Approach to Consumption