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173 lines
8.7 KiB
Markdown
173 lines
8.7 KiB
Markdown
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# Impact indicators
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This page summarises all calculated impact indicators, which are included into the [measure](/data-model/entities/other#measure) property.
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It should be noted that the impact indicators are being calculated both on the level of the research output as well on the level of distinct DOIs.
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Below we explain their main intuition, the way they are calculated, and their most important limitations, in an attempt help avoiding common pitfalls and misuses.
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## Citation Count (CC)
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***Short description:***
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This is the most widely used scientific impact indicator, which sums all citations received by each article.
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Citation count can be viewed as a measure of a publication's overall impact, since it conveys the number of other works that directly
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drew on it.
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***Algorithmic details:***
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The citation count of a
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publication $i$ corresponds to the in-degree of the corresponding node in the underlying citation network: $s_i = \sum_{j} A_{i,j}$,
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where $A$ is the adjacency matrix of the network (i.e., $A_{i,j}=1$ when paper $j$ cites paper $i$, while $A_{i,j}=0$ otherwise).
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***Parameters:*** -
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***Limitations:***
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OpenAIRE collects data from specific data sources which means that part of the existing literature may not be considered when computing this indicator.
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Also, since some indicators require the publication year for their calculation, we consider only research products for which we can gather this information from at least one data source.
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***Environment:*** PySpark
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***References:*** -
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***Authority:*** ATHENA RC • ***License:*** GPL-2.0 • ***Code:*** [BIP! Ranker](https://github.com/athenarc/Bip-Ranker)
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## "Incubation" Citation Count (iCC)
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***Short description:***
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This measure is essentially a time-restricted version of the citation count, where the time window is distinct for each paper, i.e.,
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only citations $y$ years after its publication are counted.
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***Algorithmic details:***
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The "incubation" citation count of a paper $i$ is
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calculated as: $s_i = \sum_{j,t_j \leq t_i+y} A_{i,j}$, where $A$ is the adjacency matrix and $t_j, t_i$ are the citing and cited paper's
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publication years, respectively. $t_i$ is cited paper $i$'s publication year. iCC can be seen as an indicator of a paper's initial momentum
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(impulse) directly after its publication.
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***Parameters:***
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$y=3$
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***Limitations:***
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OpenAIRE collects data from specific data sources which means that part of the existing literature may not be considered when computing this indicator.
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Also, since some indicators require the publication year for their calculation, we consider only research products for which we can gather this information from at least one data source.
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***Environment:*** PySpark
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***References:***
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* Vergoulis, T., Kanellos, I., Atzori, C., Mannocci, A., Chatzopoulos, S., Bruzzo, S. L., Manola, N., & Manghi, P. (2021, April). Bip! db: A dataset of impact measures for scientific publications. In Companion Proceedings of the Web Conference 2021 (pp. 456-460).
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***Authority:*** ATHENA RC • ***License:*** GPL-2.0 • ***Code:*** [BIP! Ranker](https://github.com/athenarc/Bip-Ranker)
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## PageRank (PR)
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***Short description:***
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Originally developed to rank Web pages, PageRank has been also widely used to rank publications in citation
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networks. In this latter context, a publication's PageRank
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score also serves as a measure of its influence.
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***Algorithmic details:***
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The PageRank score of a publication is calculated
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as its probability of being read by a researcher that either randomly selects publications to read or selects
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publications based on the references of her latest read. Formally, the score of a publication $i$ is given by:
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$$
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s_i = \alpha \cdot \sum_{j} P_{i,j} \cdot s_j + (1-\alpha) \cdot \frac{1}{N}
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$$
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where $P$ is the stochastic transition matrix, which corresponds to the column normalised version of adjacency
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matrix $A$, $\alpha \in [0,1]$, and $N$ is the number of publications in the citation network. The first addend
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of the equation corresponds to the selection (with probability $\alpha$) of following a reference, while the
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second one to the selection of randomly choosing any publication in the network. It should be noted that the
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score of each publication relies of the score of publications citing it (the algorithm is executed iteratively
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until all scores converge). As a result, PageRank differentiates citations based on the importance of citing
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articles, thus alleviating the corresponding issue of the Citation Count.
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***Parameters:***
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$\alpha = 0.5, convergence\_error = 10^{-12}$
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***Limitations:***
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OpenAIRE collects data from specific data sources which means that part of the existing literature may not be considered when computing this indicator.
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Also, since some indicators require the publication year for their calculation, we consider only research products for which we can gather this information from at least one data source.
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***Environment:*** PySpark
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***References:***
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* Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab.
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***Authority:*** ATHENA RC • ***License:*** GPL-2.0 • ***Code:*** [BIP! Ranker](https://github.com/athenarc/Bip-Ranker)
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## RAM
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***Short description:***
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RAM is essentially a modified Citation Count, where recent citations are considered of higher importance compared to older ones.
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Hence, it better captures the popularity of publications. This "time-awareness" of citations
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alleviates the bias of methods like Citation Count and PageRank against recently published articles, which have
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not had "enough" time to gather as many citations.
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***Algorithmic details:***
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The RAM score of each paper $i$ is calculated as follows:
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$$
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s_i = \sum_j{R_{i,j}}
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$$
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where $R$ is the so-called Retained Adjacency Matrix (RAM) and $R_{i,j}=\gamma^{t_c-t_j}$ when publication $j$ cites publication
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$i$, and $R_{i,j}=0$ otherwise. Parameter $\gamma \in (0,1)$, $t_c$ corresponds to the current year and $t_j$ corresponds to the
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publication year of citing article $j$.
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***Parameters:***
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$\gamma = 0.6$
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***Limitations:***
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OpenAIRE collects data from specific data sources which means that part of the existing literature may not be considered when computing this indicator.
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Also, since some indicators require the publication year for their calculation, we consider only research products for which we can gather this information from at least one data source.
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***Environment:*** PySpark
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***References:***
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* Ghosh, R., Kuo, T. T., Hsu, C. N., Lin, S. D., & Lerman, K. (2011, December). Time-aware ranking in dynamic citation networks. In 2011 ieee 11^{th} international conference on data mining workshops (pp. 373-380). IEEE.
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***Authority:*** ATHENA RC • ***License:*** GPL-2.0 • ***Code:*** [BIP! Ranker](https://github.com/athenarc/Bip-Ranker)
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## AttRank
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***Short description:***
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AttRank is a PageRank variant that alleviates its bias against recent publications (i.e., it is tailored to capture popularity).
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AttRank achieves this by modifying PageRank's probability of randomly selecting a publication. Instead of using a uniform probability,
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AttRank defines it based on a combination of the publication's age and the citations it received in recent years.
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***Algorithmic details:***
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The AttRank score
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of each publication $i$ is calculated based on:
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$$
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s_i = \alpha \cdot \sum_{j} P_{i,j} \cdot s_j
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+ \beta \cdot Att(i)+ \gamma \cdot c \cdot e^{-\rho \cdot (t_c-t_i)}
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$$
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where $\alpha + \beta + \gamma =1$ and $\alpha,\beta,\gamma \in [0,1]$. $Att(i)$ denotes a recent attention-based score for publication $i$,
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which reflects its share of citations in the $y$ most recent years, $t_i$ is the publication year of article $i$, $t_c$ denotes the current
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year, and $c$ is a normalisation constant. Finally, $P$ is the stochastic transition matrix.
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***Parameters:***
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$\alpha = 0.2, \beta = 0.5, \gamma = 0.3, \rho = -0.16, convergence\_error = 10^-{12}$
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Note that recent attention is based on the 3 most recent years (including current one).
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***Limitations:***
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OpenAIRE collects data from specific data sources which means that part of the existing literature may not be considered when computing this indicator.
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Also, since some indicators require the publication year for their calculation, we consider only research products for which we can gather this information from at least one data source.
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***Environment:*** PySpark
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***References:***
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* Kanellos, I., Vergoulis, T., Sacharidis, D., Dalamagas, T., & Vassiliou, Y. (2021, April). Ranking papers by their short-term scientific impact. In 2021 IEEE 37th International Conference on Data Engineering (ICDE) (pp. 1997-2002). IEEE.
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***Authority:*** ATHENA RC • ***License:*** GPL-2.0 • ***Code:*** [BIP! Ranker](https://github.com/athenarc/Bip-Ranker)
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