Topic evolution is a recently introduced field of research as a substitute for a more traditional text-based topic evolution, allowing the tracking of more complex evolutionary events with the use of network structures. Network-based topic evolution showed that the neighborhood characteristics of newly introduced topics can be utilized to determine when a topic would emerge in a given domain. Predicting emerging topics requires a method for generating pseudo-neighbors of previously unseen topics as the neighborhood for an emerging topic is not known before its appearance. The authors proposed the Descendant-Aware Clustering algorithm to generate a set of neighborhood candidates for future emerging topics, surpassing existing algorithms in both performances and computation times. Optimizing the algorithm parameters enhances the performance even further. Significant performance improvements were observed when NSGA-III multi-objective algorithm was applied to over 100 research domains. A set of enhanced default values are introduced to the proposed algorithm removing the necessity for dataset-specific optimization, cementing the position of the Descendant-Aware Clustering as the best clustering algorithm for detecting ancestors of future emerging topics.

Topic evolution automatically tracks a set of concepts within a given dataset over time, assisting researchers to overview various research domains. Network-based topic evolution is one of the recent approaches incorporating relational models instead of traditional text-based models for allowing the detection of topic correlation events. Topics are represented with co-occurrence relationships instead of word vectors, connected over time through their positions in a network instead of their semantic similarities. This paper shows that the topics and their network representations share meaningfully similar semantics. The existence of such contextual relationships allows topics to be labeled without having enough direct textual appearances in the document collection. Forty fields-of-study keywords with 64,215 to 5.8 million related articles were selected from the Microsoft Academic Graph dataset containing more than 200 million publications. The semantics of topics within forty topic networks were found using three sets of word embeddings trained from a collection of 10.5 million Medline abstracts from the year 2000 to 2016, and word embeddings of the topics are compared against their network-based representations, which are their neighborhoods in the previous timeslot. Cosine similarities between topics and their neighbors consistently resulted in moderate correlations from the year 2001 to 2015, showing higher values for topics that were already present in the topic networks compared to newly emerging topics. The result suggested that labeling topics based on the network structure are possible without semantic analysis, which is necessary for predicting topic evolutions such as the topic emergence when future documents are unavailable.

Researchers often rely on the byline order in a publication to estimate relative contributions made by its authors, an assumption on which existing author contribution measures are based. This byline-based approach is, however, incompatible with the alphabetical author ordering, a practice still employed by many research fields. Manually requesting authors to state their contributions can overcome the limitation of the existing methods. Such approaches, however, require resource-intensive data acquisition and preprocessing, rendering them ungeneralizable to existing bodies of bibliographic records. The present paper proposed a possibility of order-independent automatic author contribution measure by focusing on distinguishing the main contributors from the rest of the authors using machine learning algorithms, bypassing the limitation of both the byline-based numerical author contribution methods and ungeneralizable manual approaches. The experiment validated the proposed approach by successfully classifying both the primary and the corresponding authors shown as the first and the last author without utilizing byline orders. The Random Forest classifier showed the best performances, successfully classifying the first author, the last author, and both with the accuracy of 0.90, 0.89, and 0.76 respectively.

Understanding the current research topics and their histories allow researchers to focus their capabilities on the current research trends. The field of topic evolution helps the understanding by automatically model and detect the set of shared research fields in the academic papers as topics. The authors propose a novel topic evolution method for identifying and predicting the emergence of new topics under the assumption that neighborhoods of new topics in the future have distinguishable structural features. Eight journals were selected from the Microsoft Academic Graph dataset, each representing topics networks with varying size, history, and research domains. Both retrospective classification and prospective prediction showed promising performance with classifications above 0.89 for six journals and coefficients of determination exceeding 0.95 for five journals. The result showed both the retrospective identification and the prospective prediction can be done, validating the assumption that topic evolution events can be predicted with a network-based approach.

Quantifying the different contributions of co-authors is a challenging task, and existing methods often rely on the assumption that orders of author bylines reflect the contribution of co-authors. Byline-based approaches, however, render them incompatible to alphabetically-ordered bylines where authors are ordered by names, not by their contributions. The authors proposed a citation-based author contribution measure citeRatio independent of byline orders while capturing the importance of the first and last authors.