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Automatically assembling a full census of an academic field
The composition of the scientific workforce shapes the direction of
scientific research, directly through the selection of questions to
investigate, and indirectly through its influence on the training of future
scientists. In most fields, however, complete census information is difficult
to obtain, complicating efforts to study workforce dynamics and the effects of
policy. This is particularly true in computer science, which lacks a single,
all-encompassing directory or professional organization. A full census of
computer science would serve many purposes, not the least of which is a better
understanding of the trends and causes of unequal representation in computing.
Previous academic census efforts have relied on narrow or biased samples, or on
professional society membership rolls. A full census can be constructed
directly from online departmental faculty directories, but doing so by hand is
prohibitively expensive and time-consuming. Here, we introduce a topical web
crawler for automating the collection of faculty information from web-based
department rosters, and demonstrate the resulting system on the 205
PhD-granting computer science departments in the U.S. and Canada. This method
constructs a complete census of the field within a few minutes, and achieves
over 99% precision and recall. We conclude by comparing the resulting 2017
census to a hand-curated 2011 census to quantify turnover and retention in
computer science, in general and for female faculty in particular,
demonstrating the types of analysis made possible by automated census
construction.Comment: 11 pages, 6 figures, 2 table
Graphs in machine learning: an introduction
Graphs are commonly used to characterise interactions between objects of
interest. Because they are based on a straightforward formalism, they are used
in many scientific fields from computer science to historical sciences. In this
paper, we give an introduction to some methods relying on graphs for learning.
This includes both unsupervised and supervised methods. Unsupervised learning
algorithms usually aim at visualising graphs in latent spaces and/or clustering
the nodes. Both focus on extracting knowledge from graph topologies. While most
existing techniques are only applicable to static graphs, where edges do not
evolve through time, recent developments have shown that they could be extended
to deal with evolving networks. In a supervised context, one generally aims at
inferring labels or numerical values attached to nodes using both the graph
and, when they are available, node characteristics. Balancing the two sources
of information can be challenging, especially as they can disagree locally or
globally. In both contexts, supervised and un-supervised, data can be
relational (augmented with one or several global graphs) as described above, or
graph valued. In this latter case, each object of interest is given as a full
graph (possibly completed by other characteristics). In this context, natural
tasks include graph clustering (as in producing clusters of graphs rather than
clusters of nodes in a single graph), graph classification, etc. 1 Real
networks One of the first practical studies on graphs can be dated back to the
original work of Moreno [51] in the 30s. Since then, there has been a growing
interest in graph analysis associated with strong developments in the modelling
and the processing of these data. Graphs are now used in many scientific
fields. In Biology [54, 2, 7], for instance, metabolic networks can describe
pathways of biochemical reactions [41], while in social sciences networks are
used to represent relation ties between actors [66, 56, 36, 34]. Other examples
include powergrids [71] and the web [75]. Recently, networks have also been
considered in other areas such as geography [22] and history [59, 39]. In
machine learning, networks are seen as powerful tools to model problems in
order to extract information from data and for prediction purposes. This is the
object of this paper. For more complete surveys, we refer to [28, 62, 49, 45].
In this section, we introduce notations and highlight properties shared by most
real networks. In Section 2, we then consider methods aiming at extracting
information from a unique network. We will particularly focus on clustering
methods where the goal is to find clusters of vertices. Finally, in Section 3,
techniques that take a series of networks into account, where each network i
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