Shifu: Deep Learning Based Advisor-advisee Relationship Mining in Scholarly Big Data
نویسندگان
چکیده
Scholars in academia are involved in various social relationships such as advisor-advisee relationships. The analysis of such relationship can provide invaluable information for understanding the interactions among scholars as well as providing many researcher-specific applications such as advisor recommendation and academic rising star identification. However, in most cases, high quality advisor-advisee relationship dataset is unavailable. To address this problem, we propose Shifu, a deep-learning-based advisor-advisee relationship identification method which takes into account both the local properties and network characteristics. In particular, we explore how to crawl advisor-advisee pairs from PhDtree project and extract their publication information by matching them with DBLP dataset as the experimental dataset. To the best of our knowledge, no prior effort has been made to address the scientific collaboration network features for relationship identification by exploiting deep learning. Our experiments demonstrate that the proposed method outperforms other state-of-the-art machine learning methods in precision (94%). Furthermore, we apply Shifu to the entire DBLP dataset and obtain a large-scale advisor-advisee relationship dataset.
منابع مشابه
Analysis of an Advisor–Advisee Relationship: An Exploratory Study of the Area of Exact and Earth Sciences in Brazil
Scientific collaboration has been studied by researchers for decades. Several approaches have been adopted to address the question of how collaboration has evolved in terms of publication output, numbers of coauthors, and multidisciplinary trends. One particular type of collaboration that has received very little attention concerns advisor and advisee relationships. In this paper, we examine th...
متن کاملMining Typhoon Knowledge with Neural Networks
Neural network technology has not been fully utilized in data mining. The reason is two-sided. First, most neural algorithms need long-term training, and could not perform incremental learning. Second, the knowledge learned by neural networks is concealed in a large amount of connections. In this paper, we develop a neural network method to mine typhoon knowledge through overcoming those two ob...
متن کاملQuerying Web Data
As data expands into big data, enhanced or entirely novel data mining algorithms often become necessary. The real value of big data is often only exposed when we can adequately mine and learn from it. We provide an overview of new scalable techniques for knowledge discovery. Our focus is on the areas of cloud data mining and machine learning, semi-supervised processing, and deep learning. We al...
متن کاملText feature extraction based on deep learning: a review
Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, de...
متن کاملDL4MD: A Deep Learning Framework for Intelligent Malware Detection
In the Internet-age, malware poses a serious and evolving threat to security, making the detection of malware of utmost concern. Many research efforts have been conducted on intelligent malware detection by applying data mining and machine learning techniques. Though great results have been obtained with these methods, most of them are built on shallow learning architectures, which are still so...
متن کامل