Network analysis of temporal trends in scholarly research productivity

Kim, Hyoungshick; Yoon, Ji Won; Crowcroft, Jon

doi:10.1016/j.joi.2011.05.006

Cited by 12 publications

(6 citation statements)

References 28 publications

(28 reference statements)

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“…Temporal publication trend analysis Song et al 2020is another area of research where authors have used DBLP and similar datasets. In Kim et al (2012), the authors analyze the pattern of publications which eventually help them to identify the scientific output of the research groups. The evolution of communities of researchers with similar topics and interests over a selected time frame is done in Song et al (2014).…”

Section: Related Workmentioning

confidence: 99%

An On-line Analytical Processing (OLAP) Aggregation Function for Rising Aspects in Collaboration and Social Networks

Nawaz

Khan

Siddiqui

2020

Journal of Computer Science

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The overwhelming usage of social and collaboration networks provides the opportunity to analyze evolution of relationships among individuals, like celebrities or co-authors. Discovering such phenomenon in large complex networks is non-trivial due to their large sizes. In this situation, the aggregation functions used in OLAP, are useful to analyze the summarized data. OLAP has successfully proven its worth on multidimensional or complex networks. However, existing aggregations in the current OLAP systems do not produce versatile results in case of social and collaboration networks. This happens because said type of networks have structural connectivity/links among nodes, which cannot be considered by OLAP during its execution. In this situation, a useful discovery in terms of identifying pairs of nodes whose relationships is emerging in recent time, is missed. Such discovery of pairs of nodes is important for various applications such as targeted marketing, future joint partnerships and predicting future correspondence to name a few. In this study, we call such pairs as Rising_Pairs and propose an aggregation function for performing OLAP on network data whose historical information is maintained over a period of times. Using structural information, Rising_Pairs, our proposed aggregation function, discovers the strongly coupled pairs in a network data by emphasizing their recent interactions and attribute similarities. In this way, useful information related to strongly coupled pairs in a network is identified. To verify the effectiveness of our proposal, we implemented it on various types of realworld networks like Facebook, Digital Bibliography and Library Project (DBLP) and Global Positioning System (GPS) trajectory datasets and observed interesting patterns.

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Section: Related Workmentioning

confidence: 99%

An On-line Analytical Processing (OLAP) Aggregation Function for Rising Aspects in Collaboration and Social Networks

Nawaz

Khan

Siddiqui

2020

Journal of Computer Science

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“…c.append((sc, fc, vc)) 11: if fc = fa then (sa, fa, va) ← get(a) 12: a : q q 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 wMax = 24 b : q q 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 (4, 5, 5), (5, 6, 3), (6, 7, 4), (7, 8, 1), (9, 10, 2), (11,12,3), (13,14,5), (14,15,7), (15,16,2), (16,17,1), (17,18,6), (18,19,1), (19,20,2), (20, 21, 1)] p = [ (2,3,8), (4,5,6), (6,7,3), (14,15,10), (17,18,5), (19, 20, 1)]…”

Section: B Operationalizationmentioning

confidence: 99%

An algebraic approach to temporal network analysis based on temporal quantities

Batagelj

Praprotnik

2016

Soc. Netw. Anal. Min.

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In a temporal network, the presence and activity of nodes and links can change through time. To describe temporal networks we introduce the notion of temporal quantities. We define the addition and multiplication of temporal quantities in a way that can be used for the definition of addition and multiplication of temporal networks. The corresponding algebraic structures are semirings. The usual approach to (data) analysis of temporal networks is to transform it into a sequence of time slices -static networks corresponding to selected time intervals and analyze each of them using standard methods to produce a sequence of results. The approach proposed in this paper enables us to compute these results directly. We developed fast algorithms for the proposed operations. They are available as an open source Python library TQ (Temporal Quantities) and a program Ianus. The proposed approach enables us to treat as temporal quantities also other network characteristics such as degrees, connectivity components, centrality measures, Pathfinder skeleton, etc. To illustrate the developed tools we present some results from the analysis of Franzosi's violence network and Corman's Reuters terror news network.

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“…Betweenness Centrality [11,12] Through the understanding of centrality concept of graph theory and previous applications, the relation between centrality concept and the role of the parameters in the graphical models of the parameters interaction is summarized in the following table.…”

Section: Definitionmentioning

confidence: 99%

Roles of the influential parameters in the incineration process using centrality concept of graph theory

Awatif

Sabariah

Rashid

et al. 2014

AIP Conference Proceedings

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The dioxin furan is byproducts of the incineration process in which becomes a major concern to the public. In this paper, the role of the influential parameters affecting the Dioxin Furan Emission (DFE) in the incineration process was discussed. A total of seven selected incinerators in Malaysia were considered in the study. The incineration plant was categorized into the type of waste incinerated during the process, i.e. sludge and biomedical waste. Six parameters comprise of temperature, nitrogen oxide, sulphur dioxide, carbon dioxide, oxygen and moisture content were identified as the influential parameters affecting the DFE of the incineration process. Multiple Linear Regression (MLR) models were initially developed to relate the DFE and the influential parameters in each category of incinerators. These models served as the basis for the construction of the graphical models representing the interaction of the influential parameters in the process. Centrality concept was then used on these graphical models to describe the role of the parameters in the process.

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Network analysis of temporal trends in scholarly research productivity

Cited by 12 publications

References 28 publications

An On-line Analytical Processing (OLAP) Aggregation Function for Rising Aspects in Collaboration and Social Networks

An On-line Analytical Processing (OLAP) Aggregation Function for Rising Aspects in Collaboration and Social Networks

An algebraic approach to temporal network analysis based on temporal quantities

Roles of the influential parameters in the incineration process using centrality concept of graph theory

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