Xiaomei Bai

Context-Based Collaborative Filtering for Citation Recommendation

Citation recommendation is an interesting and significant research area as it solves the information overload in academia by automatically suggesting relevant references for a research paper. Recently, with the rapid proliferation of information technology, research papers are rapidly published in various conferences and journals. This makes citation recommendation a highly important and challenging discipline. In this paper, we propose a novel citation recommendation method that uses only easily obtained citation relations as source data. The rationale underlying this method is that, if two citing papers are significantly co-occurring with the same citing paper(s), they should be similar to some extent. Based on the above rationale, an association mining technique is employed to obtain the paper representation of each citing paper from the citation context. Then, these paper representations are pairwise compared to compute similarities between the citing papers for collaborative filtering. We evaluate our proposed method through two relevant real-world data sets. Our experimental results demonstrate that the proposed method significantly outperforms the baseline method in terms of precision, recall, and F1, as well as mean average precision and mean reciprocal rank, which are metrics related to the rank information in the recommendation list.

Identifying Anomalous Citations for Objective Evaluation of Scholarly Article Impact.

Evaluating the impact of a scholarly article is of great significance and has attracted great attentions. Although citation-based evaluation approaches have been widely used, these approaches face limitations e.g. in identifying anomalous citations patterns. This negligence would inevitably cause unfairness and inaccuracy to the article impact evaluation. In this study, in order to discover the anomalous citations and ensure the fairness and accuracy of research outcome evaluation, we investigate the citation relationships between articles using the following factors: collaboration times, the time span of collaboration, citing times and the time span of citing to weaken the relationship of Conflict of Interest (COI) in the citation network. Meanwhile, we study a special kind of COI, namely suspected COI relationship. Based on the COI relationship, we further bring forward the COIRank algorithm, an innovative scheme for accurately assessing the impact of an article. Our method distinguishes the citation strength, and utilizes PageRank and HITS algorithms to rank scholarly articles comprehensively. The experiments are conducted on the American Physical Society (APS) dataset. We find that about 80.88% articles contain contributed citations by co-authors in 26,366 articles and 75.55% articles among these articles are cited by the authors belonging to the same affiliation, indicating COI and suspected COI should not be ignored for evaluating impact of scientific papers objectively. Moreover, our experimental results demonstrate COIRank algorithm significantly outperforms the state-of-art solutions. The validity of our approach is verified by using the probability of Recommendation Intensity.

CocaRank: A Collaboration Caliber-based Method for Finding Academic Rising Stars

Evaluating the scientific impact of scholars has been studied by researchers from various disciplines for a long time. However, very few efforts have been devoted to evaluate the future potential of researchers based on their performance at the initial stage of scientific careers. Academic rising stars represent junior researchers who may not be very outstanding among the peers at the initial stage of their careers, but tend to become influential scholars in the future. In this paper, we propose a novel method named CocaRank, which integrates our proposed new indicator called the collaboration caliber, the typical indicator citation counts and hybrid calculation results on heterogeneous academic networks, to find academic rising stars. In addition, we investigate the appropriate time interval for the prediction of rising stars. The experimental results on real datasets demonstrate that our method can find more top ranked rising stars with higher average citation counts than other state-of-art methods.

Who are the Rising Stars in Academia

This paper proposes a novel method named ScholarRank to evaluate the scientific impact of rising stars. Our proposed ScholarRank integrates the merits of both statistical indicators and influence calculation algorithms in heterogeneous academic networks. The ScholarRank method considers three factors, which are the citation counts of authors, the mutual influence among coauthors and the mutual reinforce process among different entities in heterogeneous academic networks. Through experiments on real datasets, we demonstrate that our ScholarRank can efficiently select more top ranking rising stars than other methods.

PNCOIRank: Evaluating the Impact of Scholarly Articles with Positive and Negative Citations

Evaluating the impact of an article is a significant topic and has attracted extensive attention. Citation-based assessment methods currently face a limitation, i.e. the anomalous citations patterns still remain poorly understand. To remedy this drawback, we propose a Positive and Negative Conflict of Interest (COI)-based Rank algorithm, named PNCOIRank, to acquire positive COI, negative COI, positive suspected COI and negative suspected COI relationships. We investigate the citation relationships by the following scholarly factors: citing times, the interval of citing time, collaboration times, the interval of collaboration time, and team of citing authors with the purpose of weakening the COI relationships in citation network. A weighted PageRank is finally constructed and employed, with HITS algorithm to assess the impact of articles. Through experiments on American Physical Society (APS) dataset, we show that PNCOIRank significantly outperforms the existing methods in terms of recommendation intensity.

Trust-aware recommendation for improving aggregate diversity

Recommender systems are becoming increasingly important and prevalent because of the ability of solving information overload. In recent years, researchers are paying increasing attention to aggregate diversity as a key metric beyond accuracy, because improving aggregate recommendation diversity may increase long tails and sales diversity. Trust is often used to improve recommendation accuracy. However, how to utilize trust to improve aggregate recommendation diversity is unexplored. In this paper, we focus on solving this problem and propose a novel trust-aware recommendation method by incorporating time factor into similarity computation. The rationale underlying the proposed method is that, trustees with later creation time of trust relation can bring more diverse items to recommend to their trustors than other trustees with earlier creation time of trust relation. Through relevant experiments on publicly available dataset, we demonstrate that the proposed method outperforms the baseline method in terms of aggregate diversity while maintaining almost the same recall.

Exploring time factors in measuring the scientific impact of scholars

Taking advantage of the easy access to the rich and massive scholarly data, more and more researchers are focusing on the studies of analyzing and utilizing the scholarly big data. Among them, evaluating the scientific impact of scholars has significant importance. Measuring the scientific impact of scholars can not only provide basis for the applications of academic foundations and awards, but also shed light on the research directions for scholars. Currently, citation based methods and network based metrics are the most commonly used ways to evaluate the scientific impact. However, these approaches ignore several important facts, i.e. the dynamics of citations and the initial qualities of different articles. To alleviate the shortcomings of them, we propose a Time-aware Ranking algorithm (TRank) to evaluate the impact of scholars. Due to scholars’ sustainable supreme concerns of academic innovations, the TRank algorithm gives more credits to the newly published scholarly papers as well as their references according to the representative time functions. Our method also combines the merits of random walk algorithms and heterogeneous network topology, i.e. the mutual influences among different scholarly entities in heterogeneous academic networks. To validate the suitable time function for TRank algorithm and explore its performance, we construct the experiments on two real datasets: (1) Digitial Bibliography and Library Project, and (2) American Physical Society. The experimental results demonstrate that our algorithm outperforms other methods in selecting outstanding scholars and the evaluation results on the overall impact of scholars.

Influence analysis of Github repositories

With the support of cloud computing techniques, social coding platforms have changed the style of software development. Github is now the most popular social coding platform and project hosting service. Software developers of various levels keep entering Github, and use Github to save their public and private software projects. The large amounts of software developers and software repositories on Github are posing new challenges to the world of software engineering. This paper tries to tackle one of the important problems: analyzing the importance and influence of Github repositories. We proposed a HITS based influence analysis on graphs that represent the star relationship between Github users and repositories. A weighted version of HITS is applied to the overall star graph, and generates a different set of top influential repositories other than the results from standard version of HITS algorithm. We also conduct the influential analysis on per-month star graph, and study the monthly influence ranking of top repositories.

Evaluating the Impact of Articles with Geographical Distances between Institutions

Evaluating the impact of scholarly papers plays an important role for addressing recruitment decision, funding allocation and promotion, etc. Yet little is known how actual geographic distance influences the impact of scholarly papers. In this paper, we leverage the law of geographic distance and citations between different institutions to weight quantum Pagerank algorithm for objectively measuring the impact of scholarly papers. The results indicate that the weighted quantum PageRank algorithm can better differentiate the impact of scholarly papers compared to PageRank algorithm.

The Role of Positive and Negative Citations in Scientific Evaluation

Quantifying the impact of scientific papers objectively is crucial for research output assessment, which subsequently affects institution and country rankings, research funding allocations, academic recruitment, and national/international scientific priorities. While most of the assessment schemes based on publication citations may potentially be manipulated through negative citations, in this paper, we explore the conflict of interest (COI) relationships and discover negative citations and subsequently weaken the associated citation strength. Positive and negative COI- distinguished objective rank algorithm (PANDORA) has been developed, which captures the positive and negative COI, together with the positive and negative suspected COI relationships. In order to alleviate the influence caused by negative COI relationship, collaboration times, collaboration time span, citation times, and citation time span are employed to determine the citing strength; while for positive COI relationship, we regard it as normal citation relationship. Furthermore, we calculate the impact of scholarly papers by PageRank and HITS algorithms, based on a credit allocation algorithm which is utilized to assess the impact of institutions fairly and objectively. Experiments are conducted on the publication data set from American Physical Society data set, and the results demonstrate that our method significantly outperforms the current solutions in recommendation intensity of list R at top-K and Spearman’s rank correlation coefficient at top-K.