Searching for Rising Stars in Bibliography Networks

Li, Xiaoli; Foo, Chuan Sheng; Tew, Kar Leong; Ng, See-Kiong

doi:10.1007/978-3-642-00887-0_25

Cited by 47 publications

(24 citation statements)

References 5 publications

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“…If a junior researcher is able to work together with an expert or capable to perform numerous contributions in team work then he/she has bright chances to be a future expert (Li et al 2009). Consider two authors g and h with 4 and 3 publications respectively.…”

Section: Author Influence (Ai)mentioning

confidence: 98%

“…There are known research dimensions like finding experts (Daud et al 2010), research collaborations (Guns et al 2014), name disambiguation (Huang et al 2013), citation content analysis ) and rising stars (Li et al 2009) in academic social network. Predicting rising stars emerges as a new research area and there is little work done in this regard.…”

Section: Introductionmentioning

confidence: 99%

“…Predicting rising stars emerges as a new research area and there is little work done in this regard. An effort (Li et al 2009) was made to address the problem but limited only to incorporate just author mutual influence and static ranking of publication venues as major features. Later, a methodology (Tsatsaronis et al 2011) was introduced to address the issue of modeling the dynamics of authors' research profiles.…”

Section: Introductionmentioning

confidence: 99%

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Using machine learning techniques for rising star prediction in co-author network

et al. 2014

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Online bibliographic databases are powerful resources for research in data mining and social network analysis especially co-author networks. Predicting future rising stars is to find brilliant scholars/researchers in co-author networks. In this paper, we propose a solution for rising star prediction by applying machine learning techniques. For classification task, discriminative and generative modeling techniques are considered and two algorithms are chosen for each category. The author, co-authorship and venue based information are incorporated, resulting in eleven features with their mathematical formulations. Extensive experiments are performed to analyze the impact of individual feature, category wise and their combination w.r.t classification accuracy. Then, two ranking lists for top 30 scholars are presented from predicted rising stars. In addition, this concept is demonstrated for prediction of rising stars in database domain. Data from DBLP and Arnetminer databases (1996-2000 for wide disciplines) are used for algorithms' experimental analysis.

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Section: Author Influence (Ai)mentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using machine learning techniques for rising star prediction in co-author network

et al. 2014

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“…In the later category, e.g., the work in [2], nodes correspond to authors, and edges represent citation or co-authorship relation. The resulting representation can be used to rank authors, find author communities, measure author centrality [3,4,6], or find special relations between authors, such as advisor-advisee [10]. Regarding the evolution analysis of graphs, snapshot-based approaches, e.g., an author-paper graph per year, are frequently used [1,8].…”

Section: Mining Graphs From Bibliographical Databasesmentioning

confidence: 99%

How to Become a Group Leader? or Modeling Author Types Based on Graph Mining

Tsatsaronis

Varlamis

Torge

et al. 2011

Research and Advanced Technology for Digital Libraries

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Abstract. Bibliographic databases are a prosperous field for data mining research and social network analysis. The representation and visualization of bibliographic databases as graphs and the application of data mining techniques can help us uncover interesting knowledge regarding how the publication records of authors evolve over time. In this paper we propose a novel methodology to model bibliographical databases as Power Graphs, and mine them in an unsupervised manner, in order to learn basic author types and their properties through clustering. The methodology takes into account the evolution of the co-authorship information, the volume of published papers over time, as well as the impact factors of the venues hosting the respective publications. As a proof of concept of the applicability and scalability of our approach, we present experimental results in the DBLP data.

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“…In recent years, many real‐world networks, such as the World Wide Web (Albert, Jeong, & Barabási, ), social networks (Li, Foo, Tew, & Ng, ; Wasserman & Faust, ), biological networks (Li, Foo, & Ng, ; Li, Tan, Foo, & Ng, ; Li, Wu, Kwoh, & Ng, ; Palla, Derényi, Farkas, & Vicsek, ; Steinhaeuser & Chawla, ; Wu, Li, Kwoh, & Ng, ), citation networks (Redner, ), and communication networks (Nisheeth, Anirban, & Rastogi, ) have become available for data mining. A key task in mining these networks is to find the underlying communities, where a community is a group of people or objects that share some common interests.…”

Section: Introductionmentioning

confidence: 99%

Community detection based on social interactions in a social network

Chen

Chuang

Chiu

2014

Asso for Info Science & Tech

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Recent research has involved identifying communities in networks. Traditional methods of community detection usually assume that the network's structural information is fully known, which is not the case in many practical networks. Moreover, most previous community detection algorithms do not differentiate multiple relationships between objects or persons in the real world. In this article, we propose a new approach that utilizes social interaction data (e.g., users' posts on Facebook) to address the community detection problem in Facebook and to find the multiple social groups of a Facebook user. Some advantages to our approach are (a) it does not depend on structural information, (b) it differentiates the various relationships that exist among friends, and (c) it can discover a target user's multiple communities. In the experiment, we detect the community distribution of Facebook users using the proposed method. The experiment shows that our method can achieve the result of having the average scores of Total-Community-Purity and Total-Cluster-Purity both at approximately 0.8.

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Searching for Rising Stars in Bibliography Networks

Cited by 47 publications

References 5 publications

Using machine learning techniques for rising star prediction in co-author network

Using machine learning techniques for rising star prediction in co-author network

How to Become a Group Leader? or Modeling Author Types Based on Graph Mining

Community detection based on social interactions in a social network

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