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A principal component analysis of 39 scientific impact measures
The impact of scientific publications has traditionally been expressed in
terms of citation counts. However, scientific activity has moved online over
the past decade. To better capture scientific impact in the digital era, a
variety of new impact measures has been proposed on the basis of social network
analysis and usage log data. Here we investigate how these new measures relate
to each other, and how accurately and completely they express scientific
impact. We performed a principal component analysis of the rankings produced by
39 existing and proposed measures of scholarly impact that were calculated on
the basis of both citation and usage log data. Our results indicate that the
notion of scientific impact is a multi-dimensional construct that can not be
adequately measured by any single indicator, although some measures are more
suitable than others. The commonly used citation Impact Factor is not
positioned at the core of this construct, but at its periphery, and should thus
be used with caution
Eigenvector-Based Centrality Measures for Temporal Networks
Numerous centrality measures have been developed to quantify the importances
of nodes in time-independent networks, and many of them can be expressed as the
leading eigenvector of some matrix. With the increasing availability of network
data that changes in time, it is important to extend such eigenvector-based
centrality measures to time-dependent networks. In this paper, we introduce a
principled generalization of network centrality measures that is valid for any
eigenvector-based centrality. We consider a temporal network with N nodes as a
sequence of T layers that describe the network during different time windows,
and we couple centrality matrices for the layers into a supra-centrality matrix
of size NTxNT whose dominant eigenvector gives the centrality of each node i at
each time t. We refer to this eigenvector and its components as a joint
centrality, as it reflects the importances of both the node i and the time
layer t. We also introduce the concepts of marginal and conditional
centralities, which facilitate the study of centrality trajectories over time.
We find that the strength of coupling between layers is important for
determining multiscale properties of centrality, such as localization phenomena
and the time scale of centrality changes. In the strong-coupling regime, we
derive expressions for time-averaged centralities, which are given by the
zeroth-order terms of a singular perturbation expansion. We also study
first-order terms to obtain first-order-mover scores, which concisely describe
the magnitude of nodes' centrality changes over time. As examples, we apply our
method to three empirical temporal networks: the United States Ph.D. exchange
in mathematics, costarring relationships among top-billed actors during the
Golden Age of Hollywood, and citations of decisions from the United States
Supreme Court.Comment: 38 pages, 7 figures, and 5 table
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