Coauthor prediction for junior researchers

Research collaboration can bring in different perspectives and generate more productive results. However, finding an appropriate collaborator can be difficult due to the lacking of sufficient information. Link prediction is a related technique for collaborator discovery; but its focus has been mostly on the core authors who have relatively more publications. We argue that junior researchers actually need more help in finding collaborators. Thus, in this paper, we focus on coauthor prediction for junior researchers. Most of the previous works on coauthor prediction considered global network feature and local network feature separately, or tried to combine local network feature and content feature. But we found a significant improvement by simply combing local network feature and global network feature. We further developed a regularization based approach to incorporate multiple features simultaneously. Experimental results demonstrated that this approach outperformed the simple linear combination of multiple features. We further showed that content features, which were proved to be useful in link prediction, can be easily integrated into our regularization approach. © 2013 Springer-Verlag

Benchmarking the Privacy-Preserving People Search

People search is an important topic in information retrieval. Many previous studies on this topic employed social networks to boost search performance by incorporating either local network features (e.g. the common connections between the querying user and candidates in social networks), or global network features (e.g. the PageRank), or both. However, the available social network information can be restricted because of the privacy settings of involved users, which in turn would affect the performance of people search. Therefore, in this paper, we focus on the privacy issues in people search. We propose simulating different privacy settings with a public social network due to the unavailability of privacy-concerned networks. Our study examines the influences of privacy concerns on the local and global network features, and their impacts on the performance of people search. Our results show that: 1) the privacy concerns of different people in the networks have different influences. People with higher association (i.e. higher degree in a network) have much greater impacts on the performance of people search; 2) local network features are more sensitive to the privacy concerns, especially when such concerns come from high association peoples in the network who are also related to the querying user. As the first study on this topic, we hope to generate further discussions on these issues.Comment: 4 pages, 5 figure

User exploration of slider facets in interactive people search system

People search is an important search task where the goal is to find people instead of documents. Providing search facets in a people search system can help users better describe their search intents. Some systems provide checkboxes for the discrete values of each facet to assist users filtering search results. Some other systems in recent studies provide sliders to represent the continuous values of facets. Slider facets enable an interactive search system to handle the facets without discrete values. Yet, the ways of how users interact with slider facets are rarely studied, particularly for people search tasks. Based on a user study with 24 participants using an interactive people search interface with three slider facets, we find that users indeed utilize the slider facets consistently in their search processes to fine-tune the search results ranking. We also find that although tuning the slider facets values can bring performance boost but users are lack of abilities to locate the optimal facet-values, which indicates the necessity of providing automatic facet-value suggestion

Tracing and Predicting Collaboration for Junior Scholars

Academic publication is a key indicator for measuring scholars' scientific productivity and has a crucial impact on their future career. Previous work has identified the positive association between the number of collaborators and academic productivity, which motivates the problem of tracing and predicting potential collaborators for junior scholars. Nevertheless, the insufficient publication record makes current approaches less effective for junior scholars. In this paper, we present an exploratory study of predicting junior scholars' future co-authorship in three different network density. By combining features based on affiliation, geographic and content information, the proposed model significantly outperforms the baseline methods by 12% in terms of sensitivity. Furthermore, the experiment result shows the association between network density and feature selection strategy. Our study sheds light on the re-evaluation of existing approaches to connect scholars in the emerging worldwide Web of Scholars

Exploring a Modelling Method with Semantic Link Network and Resource Space Model

To model the complex reality, it is necessary to develop a powerful semantic model. A rational approach is to integrate a relational view and a multi-dimensional view of reality. The Semantic Link Network (SLN) is a semantic model based on a relational view and the Resource Space Model (RSM) is a multi-dimensional view for managing, sharing and specifying versatile resources with a universal resource observation. The motivation of this research consists of four aspects: (1) verify the roles of Semantic Link Network and the Resource Space Model in effectively managing various types of resources, (2) demonstrate the advantages of the Resource Space Model and Semantic Link Network, (3) uncover the rules through applications, and (4) generalize a methodology for modelling complex reality and managing various resources. The main contribution of this work consists of the following aspects: 1. A new text summarization method is proposed by segmenting a document into clauses based on semantic discourse relations and ranking and extracting the informative clauses according to their relations and roles. The Resource Space Model benefits from using semantic link network, ranking techniques and language characteristics. Compared with other summarization approaches, the proposed approach based on semantic relations achieves a higher recall score. Three implications are obtained from this research. 2. An SLN-based model for recommending research collaboration is proposed by extracting a semantic link network of different types of semantic nodes and different types of semantic links from scientific publications. Experiments on three data sets of scientific publications show that the model achieves a good performance in predicting future collaborators. This research further unveils that different semantic links play different roles in representing texts. 3. A multi-dimensional method for managing software engineering processes is developed. Software engineering processes are mapped into multiple dimensions for supporting analysis, development and maintenance of software systems. It can be used to uniformly classify and manage software methods and models through multiple dimensions so that software systems can be developed with appropriate methods. Interfaces for visualizing Resource Space Model are developed to support the proposed method by keeping the consistency among interface, the structure of model and faceted navigation