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Ranking scientific articles is an important but challenging task, partly due to the dynamic nature of the evolving publication network. In this paper, we mainly focus on two problems: (1) how to rank articles in the heterogeneous network; and (2) how to use time information in the dynamic network in order to obtain a better ranking result. To tackle the(More)
In this paper, we present a useful data modeling methodology in data warehousing which integrates three existing approaches normally used in isolation: goal-driven, data-driven and user-driven. It comprises of four stages. Goal-driven stage produces subjects and KPIs(Key Performance Indicators) of main business fields. Data-driven stage produces subject(More)
In traditional clustering methods, a document is often represented as "bag of words" (in BOW model) or n-grams (in suffix tree document model) without considering the natural language relationships between the words. In this paper, we propose a novel approach DGDC (Dependency Graph-based Document Clustering algorithm) to address this issue. In our(More)
There is an increasing demand for real-time iterative analysis over evolving data. In this paper, we propose a novel execution model to obtain timely results at given instants. We notice that a loop starting from a good initial guess usually converges fast. Hence we organize the execution of iterative methods over evolving data into a main loop and several(More)
Scientific mapping has now become an important subject in the scientometrics field. Journal clustering can provide insights into both the internal relations among journals and the evolution trend of studies. In this paper, we apply the affinity propagation (AP) algorithm to do scientific journal clustering. The AP algorithm identifies clusters by detecting(More)
In this paper, we aim at studying the correlation between Twitter and the stock market. Specifically, we first apply non-Gaussian SVAR (structural vector autoregression) to identify possible relationships among the Twitter and stock market factors. Compared with conventional models such as Granger causality method which assume that the error items are(More)
With the renaissance of neural network in recent years, relation classification has again become a research hotspot in natural language processing, and leveraging parse trees is a common and effective method of tackling this problem. In this work, we offer a new perspective on utilizing syntactic information of dependency parse tree and present a position(More)