arXiv subjects

Yuxiao Dong

Publications and source records attributed to Yuxiao Dong.

129 records · Page 8Linked to original sources

arXiv2014

Will This Paper Increase Your h-index? Scientific Impact Prediction

Scientific impact plays a central role in the evaluation of the output of scholars, departments, and institutions. A widely used measure of scientific impact is citations, with a growing body of literature focused on predicting the number of citations obtained by any given publication. The effectiveness of such predictions, however, is fundamentally limited by the power-law distribution of citations, whereby publications with few citations are extremely common and publications with many citations are relatively rare. Given this limitation, in this work we instead address a related question asked by many academic researchers in the course of writing a paper, namely: "Will this paper increase my h-index?" Using a real academic dataset with over 1.7 million authors, 2 million papers, and 8 million citation relationships from the premier online academic service ArnetMiner, we formalize a novel scientific impact prediction problem to examine several factors that can drive a paper to increase the primary author's h-index. We find that the researcher's authority on the publication topic and the venue in which the paper is published are crucial factors to the increase of the primary author's h-index, while the topic popularity and the co-authors' h-indices are of surprisingly little relevance. By leveraging relevant factors, we find a greater than 87.5% potential predictability for whether a paper will contribute to an author's h-index within five years. As a further experiment, we generate a self-prediction for this paper, estimating that there is a 76% probability that it will contribute to the h-index of the co-author with the highest current h-index in five years. We conclude that our findings on the quantification of scientific impact can help researchers to expand their influence and more effectively leverage their position of "standing on the shoulders of giants."

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arXiv2014

Predicting Node Degree Centrality with the Node Prominence Profile

Centrality of a node measures its relative importance within a network. There are a number of applications of centrality, including inferring the influence or success of an individual in a social network, and the resulting social network dynamics. While we can compute the centrality of any node in a given network snapshot, a number of applications are also interested in knowing the potential importance of an individual in the future. However, current centrality is not necessarily an effective predictor of future centrality. While there are different measures of centrality, we focus on degree centrality in this paper. We develop a method that reconciles preferential attachment and triadic closure to capture a node's prominence profile. We show that the proposed node prominence profile method is an effective predictor of degree centrality. Notably, our analysis reveals that individuals in the early stage of evolution display a distinctive and robust signature in degree centrality trend, adequately predicted by their prominence profile. We evaluate our work across four real-world social networks. Our findings have important implications for the applications that require prediction of a node's future degree centrality, as well as the study of social network dynamics.

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arXiv2014

Microscopic Evolution of Social Networks by Triad Position Profile

Disentangling the mechanisms underlying the social network evolution is one of social science's unsolved puzzles. Preferential attachment is a powerful mechanism explaining social network dynamics, yet not able to explain all scaling-laws in social networks. Recent advances in understanding social network dynamics demonstrate that several scaling-laws in social networks follow as natural consequences of triadic closure. Macroscopic comparisons between them are discussed empirically in many works. However the network evolution drives not only the emergence of macroscopic scaling but also the microscopic behaviors. Here we exploit two fundamental aspects of the network microscopic evolution: the individual influence evolution and the process of link formation. First we develop a novel framework for the microscopic evolution, where the mechanisms of preferential attachment and triadic closure are well balanced. Then on four real-world datasets we apply our approach for two microscopic problems: node's prominence prediction and link prediction, where our method yields significant predictive improvement over baseline solutions. Finally to be rigorous and comprehensive, we further observe that our framework has a stronger generalization capacity across different kinds of social networks for two microscopic prediction problems. We unveil the significant factors with a greater degree of precision than has heretofore been possible, and shed new light on networks evolution.

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