11,279 research outputs found
The classical origin of modern mathematics
The aim of this paper is to study the historical evolution of mathematical
thinking and its spatial spreading. To do so, we have collected and integrated
data from different online academic datasets. In its final stage, the database
includes a large number (N~200K) of advisor-student relationships, with
affiliations and keywords on their research topic, over several centuries, from
the 14th century until today. We focus on two different topics, the evolving
importance of countries and of the research disciplines over time. Moreover we
study the database at three levels, its global statistics, the mesoscale
networks connecting countries and disciplines, and the genealogical level
Building the Brazilian Academic Genealogy Tree
Along the history, many researchers provided remarkable contributions to
science, not only advancing knowledge but also in terms of mentoring new
scientists. Currently, identifying and studying the formation of researchers
over the years is a challenging task as current repositories of theses and
dissertations are cataloged in a decentralized way through many local digital
libraries. Following our previous work in which we created and analyzed a large
collection of genealogy trees extracted from NDLTD, in this paper we focus our
attention on building such trees for the Brazilian research community. For
this, we use data from the Lattes Platform, an internationally renowned
initiative from CNPq, the Brazilian National Council for Scientific and
Technological Development, for managing information about individual
researchers and research groups in Brazil
The Scientist, Spring 2009
https://scholarworks.sjsu.edu/scientist/1004/thumbnail.jp
The Scientist, Spring 2009
https://scholarworks.sjsu.edu/scientist/1004/thumbnail.jp
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
The Academic Genealogy of the Scholars of the Information Systems Discipline
Despite the relative youth of our information systems discipline, many of us do not know the founders of our field or the academic “families” that have helped shape us into what we are today. We have changed greatly over the past few decades, both topically and geographically. As the Information Systems (IS) discipline enters its second half-century, now is a good time to identify the origins of our academic community while some of the founders are still with us. We have created a web-enabled “family tree” of more than 11 thousand IS scholars that investigators can use to examine where we came from, where we are going, and how we can position our field for future academic generations. The genealogical data can be used to show the trajectory of our research, the influences that shape our field, and the influences of organizational designs and the broader environment
- …