Ranking authors in digital libraries

Gollapalli, Sujatha Das; Mitra, Prasenjit; Giles, C. Lee

doi:10.1145/1998076.1998123

Cited by 32 publications

(15 citation statements)

References 23 publications

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“…The scheme is based on the number of articles co-authored by two given authors. Gollapalli et al [5] apply PageRank algorithm on the co-authorship network for ranking authors.…”

Section: Related Workmentioning

confidence: 99%

“…If the query is merely a short text, we feed the whole query to the annotator and collect the annotating topics. If the query is a large document (like the one we use for our experiment), we first segment the query into sentences using LingPipe sentence extraction tool 5 , then feed each sentence to the annotator (the annotator cannot process a large text. ).…”

Section: Extracting Topics Using Wikipediaminermentioning

confidence: 99%

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Taxonomy-based query-dependent schemes for profile similarity measurement

Tuarob

Mitra

Giles

2012

Proceedings of the 1st Joint International Workshop on Entity-Oriented and Semantic Search

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Semantic search techniques have increasingly gained attention in information retrieval literature. Authors are great sources of semantic interpretation for documents, especially in scholarly domains where articles mostly reflect the research interests of the authors. Being able to interpret semantic meanings of documents from their authors would give rise to many interesting applications, especially in academic digital library literature. In this paper, we present taxonomy-based query-dependent schemes for computing author profile similarity. Our schemes have the capability to capture partial similarities, as opposed to traditional topic overlap based approaches. We generalize our schemes so that they can be easily adopted to other application domains. We acquire resources from multiple places such as Wikipedia, Citeseer X , ArnetMiner, and WikipediaMiner as part of our work. We provide encouraging anecdotal results along with suggestions on potential applications of the proposed schemes.

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“…The scheme is based on the number of articles co-authored by two given authors. Gollapalli et al [5] apply PageRank algorithm on the co-authorship network for ranking authors.…”

Section: Related Workmentioning

confidence: 99%

Section: Extracting Topics Using Wikipediaminermentioning

confidence: 99%

Taxonomy-based query-dependent schemes for profile similarity measurement

Tuarob

Mitra

Giles

2012

Proceedings of the 1st Joint International Workshop on Entity-Oriented and Semantic Search

Self Cite

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“…Expert search and ranking problem has become an active research area in various application domains. However, it was studied in different contexts including the TREC enterprise track [9], question answering (QA) Websites [10], [5], [11], enterprises such as email communication [12], and scientific networks in digital libraries [13], [14].…”

Section: Related Workmentioning

confidence: 99%

Robust Expert Ranking in Online Communities - Fighting Sybil Attacks

Rashed

Balasoiu

Klamma

2012

Proceedings of the 8th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing

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Nowadays, many online communities provide means for users to contribute in the evaluation of community created media by tagging, commenting and rating. Judging the users expertise in such collaborative systems is an important issue. As these systems are becoming increasingly popular, they are attackable, e.g. by Sybil Attacks. Thus, an effective expert ranking strategy must be robust to such attacks. In this paper, we propose MHITS, an algorithm to rank users' expertise by exploiting the number of users' fair ratings and direct trust users gain in the online community. We integrate SumUp, a Sybil-resilient algorithm, into MHITS algorithm as a robust ranking strategy. Experimental results show the effectiveness of the proposed method, which can ensure that the highly ranked experts are highly trusted users and provide the high number of fair ratings for the relevant media. We contribute to the experimental evaluation of algorithms for online systems, fighting malicious behavior.

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“…It has long been realized that the analysis of co-authorship graphs can help us to understand the structure and evolution of corresponding academic societies. Those networks can also be used to develop models for ranking most influential authors in a database [8], to automatically determine the most appropriate reviewers for a manuscript [21], or even to predict future research collaborations [12]. Nodes in a co-authorship network represent researchers -people who published at least one research paper.…”

Section: Introductionmentioning

confidence: 99%

Exploratory Analysis of Communities in Co-authorship Networks: A Case Study

Savić

Ivanović

Radovanović

et al. 2015

Advances in Intelligent Systems and Computing

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Digital libraries enable worldwide access to scientific results, but also provide a valuable source of information that can be used to investigate patterns and trends in scientific collaboration. The Electronic Library of the Mathematical Institute of the Serbian Academy of Sciences and Arts (eLib) digitizes the most prominent mathematical journals printed in Serbia. Using eLib bibliographical records we constructed a co-authorship network representing collaborations between authors who published their papers in eLib journals in the period from 1932 to 2011. In this paper we apply community detection techniques in order to examine the structure of the eLib co-authorship network. Such study reveals characteristic patterns of scientific collaboration in Serbian mathematical journals, and helps us to understand the (self-)organization of the eLib community of authors.

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Ranking authors in digital libraries

Cited by 32 publications

References 23 publications

Taxonomy-based query-dependent schemes for profile similarity measurement

Taxonomy-based query-dependent schemes for profile similarity measurement

Robust Expert Ranking in Online Communities - Fighting Sybil Attacks

Exploratory Analysis of Communities in Co-authorship Networks: A Case Study

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