A link-based similarity measure for scientific literature

Yoon, Seok-Ho; Kim, Sang–Wook; Park, Sunju

doi:10.1145/1772690.1772880

Cited by 21 publications

(20 citation statements)

References 4 publications

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“…The link-based similarity measure is based on the proportion of common neighbors shared by the two nodes [20], [21]. So, the link-based similarity between two nodes increases when they have more common neighbors.…”

Section: Difference In Analysis Resultsmentioning

confidence: 99%

“…We rely on the precision value for ranking analysis and MAP for similarity measure. Precision describes the ratio of correct match in ranking analysis between the anonymized result and the ground truth [20]. MAP is an average precision value of node similarity measures [20].…”

Section: Cc(i)mentioning

confidence: 99%

“…Precision describes the ratio of correct match in ranking analysis between the anonymized result and the ground truth [20]. MAP is an average precision value of node similarity measures [20]. The precision & MAP values are towards 1 if the responses to the query in the anonymized networks are similar to those in the original network.…”

Section: Cc(i)mentioning

confidence: 99%

“…Second, we investigate any difference in the social networks analysis results between the original and anonymized networks. We use ranking [23] and linkbased similarity measures [20]. Table 1 demonstrates the parameter values of privacy preserving methods and the numbers of edge difference between original and anonymized networks.…”

Section: Observationmentioning

confidence: 99%

See 3 more Smart Citations

Analyzing Network Privacy Preserving Methods: A Perspective of Social Network Characteristics

Bae

LEE

KIM

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYA burst of social network services increases the need for in-depth analysis of network activities. Privacy breach for network participants is a concern in such analysis efforts. This paper investigates structural and property changes via several privacy preserving methods (anonymization) for social network. The anonymized social network does not follow the power-law for node degree distribution as the original network does. The peak-hop for node connectivity increases at most 1 and the clustering coefficient of neighbor nodes shows 6.5 times increases after anonymization. Thus, we observe inconsistency of privacy preserving methods in social network analysis. key words: network privacy preserving, privacy breach, structural disparity

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Section: Difference In Analysis Resultsmentioning

confidence: 99%

Section: Cc(i)mentioning

confidence: 99%

Section: Cc(i)mentioning

confidence: 99%

Section: Observationmentioning

confidence: 99%

See 2 more Smart Citations

Analyzing Network Privacy Preserving Methods: A Perspective of Social Network Characteristics

Bae

LEE

KIM

et al. 2014

IEICE Trans. Inf. & Syst.

Self Cite

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show abstract

“…논문과 같은 문서에 대한 유사도를 계산하는 기존 유사도 계산 방안들은 크게 텍스트 기반 유사도 계산 방안과 링크 기반 유사도 계산 방안으로 분류될 수 있다 [4]. 본 논문에서 는 텍스트 기반 유사도 계산 방안을 이용해서 논문들 간의 유사도를 계산하고자 한다.…”

Section: 서 론 1)unclassified

A Text-based Similarity Measure for Scientific Literature

Yoon¹,

Kim²

2011

The KIPS Transactions:PartD

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Identifying composite crosscutting concerns through semi-supervised learning

Zhu

Huang

Zhou

et al. 2013

Softw. Pract. Exper.

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SUMMARYAspect mining improves the modularity of legacy software systems through identifying their underlying crosscutting concerns (CCs). However, a realistic CC is a composite one that consists of CC seeds and relative program elements, which makes it a great challenge to identify a composite CC. In this paper, inspired by the state‐of‐the‐art information retrieval techniques, we model this problem as a semi‐supervised learning problem. First, the link analysis technique is adopted to generate CC seeds. Second, we construct a coupling graph, which indicates the relationship between CC seeds. Then, we adopt community detection technique to generate groups of CC seeds as constraints for semi‐supervised learning, which can guide the clustering process. Furthermore, we propose a semi‐supervised graph clustering approach named constrained authority‐shift clustering to identify composite CCs. Two measurements, namely, similarity and connectivity, are defined and seeded graph is generated for clustering program elements. We evaluate constrained authority‐shift clustering on numerous software systems including large‐scale distributed software system. The experimental results demonstrate that our semi‐supervised learning is more effective in detecting composite CCs. Copyright © 2013 John Wiley & Sons, Ltd.

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A link-based similarity measure for scientific literature

Abstract: In this paper, we propose a new approach to measure similarities among academic papers based on their references. Our similarity measure uses both in-link and out-link by transforming in-link and out-link into undirected links.

Cited by 21 publications

References 4 publications

Analyzing Network Privacy Preserving Methods: A Perspective of Social Network Characteristics

Analyzing Network Privacy Preserving Methods: A Perspective of Social Network Characteristics

A Text-based Similarity Measure for Scientific Literature

Identifying composite crosscutting concerns through semi-supervised learning

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