It is popular nowadays to bring techniques from bibliometrics and scientometrics into the world of digital libraries to explore mechanisms which underlie community development. In this paper we use the DBLP data to investigate the author's scientific career, and analyze some of the computer science communities. We compare them in terms of productivity and population stability, and use these features to compare the sets of top-ranked conferences with their lower ranked counterparts.
Besides the network property analysis there is an interest in research related to the topic development and distribution in scientific community. Z " a ine, Chen and Goebel @cite_1 used collaboration network embedded in DBLP to discover topical connections between the network members and eventually use them in a recommendation system. Another investigation connecting topics and co-authors community has been reported in @cite_3 . The work used CiteSeer as a testbed and aimed at getting insight in topic evolution and connection between the researchers and topics.
@cite_1 @cite_3
2109480754 1967719219 2139836297 2110325612
The problem of predicting links or interactions between objects in a network, is an important task in network analysis. Along this line, link prediction between co-authors in a co-author network is a frequently studied problem. In most of these studies, authors are considered in a homogeneous network, .e., only one type of objects(author type) and one type of links (co-authorship) exist in the network. However, in a real bibliographic network, there are multiple types of objects ( .g., venues, topics, papers) and multiple types of links among these objects. In this paper, we study the problem of co-author relationship prediction in the heterogeneous bibliographic network, and a new methodology called, .e., meta path-based relationship prediction model, is proposed to solve this problem. First, meta path-based topological features are systematically extracted from the network. Then, a supervised model is used to learn the best weights associated with different topological features in deciding the co-author relationships. We present experiments on a real bibliographic network, the DBLP network, which show that metapath-based heterogeneous topological features can generate more accurate prediction results as compared to homogeneous topological features. In addition, the level of significance of each topological feature can be learned from the model, which is helpful in understanding the mechanism behind the relationship building. Abstract Intellectual relationships and collaboration networks are the basis for the development of a knowledge domain. The visual representation of such “knowledge networks” contributes to the overall understanding of intellectual collaborations in a particular knowledge domain. Based on the co-authorship data from recent journal publications over a period of five years, the authors applied social network analysis to explore the network structures and identify their network properties in the hospitality research domain. The analysis revealed the core and peripheral networks where the power law distribution was observed on the pattern of publishing academic papers. The overall network was further examined by nine research streams in both “global” and “contextual” views to understand a broad variety of the collaboration patterns of hospitality researchers. Extracting information from very large collections of structured, semi-structured or even unstructured data can be a considerable challenge when much of the hidden information is implicit within relationships among entities in the data. Social networks are such data collections in which relationships play a vital role in the knowledge these networks can convey. A bibliographic database is an essential tool for the research community, yet finding and making use of relationships comprised within such a social network is difficult. In this paper we introduce DBconnect, a prototype that exploits the social network coded within the DBLP database by drawing on a new random walk approach to reveal interesting knowledge about the research community and even recommend collaborations. Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. In this paper we describe several algorithms designed for this task, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods. We compare the predictive accuracy of the various methods in a set of representative problem domains. We use two basic classes of evaluation metrics. The first characterizes accuracy over a set of individual predictions in terms of average absolute deviation. The second estimates the utility of a ranked list of suggested items. This metric uses an estimate of the probability that a user will see a recommendation in an ordered list. Experiments were run for datasets associated with 3 application areas, 4 experimental protocols, and the 2 evaluation metr rics for the various algorithms. Results indicate that for a wide range of conditions, Bayesian networks with decision trees at each node and correlation methods outperform Bayesian-clustering and vector-similarity methods. Between correlation and Bayesian networks, the preferred method depends on the nature of the dataset, nature of the application (ranked versus one-by-one presentation), and the availability of votes with which to make predictions. Other considerations include the size of database, speed of predictions, and learning time.