Influence in time-dependent citation networks

Rakoczy, Monika Ewa; Bouzeghoub, Amel; Gançarski, Alda Lopes; Węgrzyn-Wolska, Katarzyna

doi:10.1109/rcis.2018.8406647

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…It is based on the users' proactive and reactive behaviors, that can be found on basically any social networking site, and can be used with different data sets. A few interesting approaches based on ARIM were proposed: e.g., method to calculate influence in time-dependent citation networks [113], and an algorithm to predict the reputation using the known influence value [114]. Finally, a very interesting and new approach of micro-influencer was defined [112].…”

Section: General Modelmentioning

confidence: 99%

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

Zamuda

Crescimanna

Burguillo

et al. 2019

Lecture Notes in Computer Science

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This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking

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Section: General Modelmentioning

confidence: 99%

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

Zamuda

Crescimanna

Burguillo

et al. 2019

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

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“…Moreover, contrary to previous works [25], which ignore the size of the considered possible reference group, we propose to distinguish two interpretations of Citation Ratio metric, namely local and global one. While a detailed discussion on the two types can be found in our previous work [8], we will only limit ourselves to recall the two definitions. (1), where in the denominator we consider all possible communities from available data (set S is equal to universe U ).…”

Section: Citation Ratiomentioning

confidence: 99%

Time-Dependent Influence Measurement in Citation Networks

Rakoczy¹,

Bouzeghoub²,

Gançarski³

et al. 2018

CSIMQ

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In every scientific discipline, researchers face two common dilemmas: where to find bleeding-edge papers and where to publish their own articles. We propose to answer these questions by looking at the influence between communities, e.g. conferences or journals. The influential conferences are those which papers are heavily cited by other conferences, i.e. they are visible, significant and inspiring. For the task of finding such influential places-to-publish, we introduce a Running Influence model that aims to discover pairwise influence between communities and evaluate the overall influence of each considered community. We have taken into consideration time aspects such as intensity of papers citations over time and difference of conferences starting years. The community influence analysis is tested on real-world data of Computer Science conferences.

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Influence in time-dependent citation networks

Cited by 2 publications

References 31 publications

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

Time-Dependent Influence Measurement in Citation Networks

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