61,471 research outputs found
Centrality Metric for Dynamic Networks
Centrality is an important notion in network analysis and is used to measure
the degree to which network structure contributes to the importance of a node
in a network. While many different centrality measures exist, most of them
apply to static networks. Most networks, on the other hand, are dynamic in
nature, evolving over time through the addition or deletion of nodes and edges.
A popular approach to analyzing such networks represents them by a static
network that aggregates all edges observed over some time period. This
approach, however, under or overestimates centrality of some nodes. We address
this problem by introducing a novel centrality metric for dynamic network
analysis. This metric exploits an intuition that in order for one node in a
dynamic network to influence another over some period of time, there must exist
a path that connects the source and destination nodes through intermediaries at
different times. We demonstrate on an example network that the proposed metric
leads to a very different ranking than analysis of an equivalent static
network. We use dynamic centrality to study a dynamic citations network and
contrast results to those reached by static network analysis.Comment: in KDD workshop on Mining and Learning in Graphs (MLG
The first-mover advantage in scientific publication
Mathematical models of the scientific citation process predict a strong
"first-mover" effect under which the first papers in a field will, essentially
regardless of content, receive citations at a rate enormously higher than
papers published later. Moreover papers are expected to retain this advantage
in perpetuity -- they should receive more citations indefinitely, no matter how
many other papers are published after them. We test this conjecture against
data from a selection of fields and in several cases find a first-mover effect
of a magnitude similar to that predicted by the theory. Were we wearing our
cynical hat today, we might say that the scientist who wants to become famous
is better off -- by a wide margin -- writing a modest paper in next year's
hottest field than an outstanding paper in this year's. On the other hand,
there are some papers, albeit only a small fraction, that buck the trend and
attract significantly more citations than theory predicts despite having
relatively late publication dates. We suggest that papers of this kind, though
they often receive comparatively few citations overall, are probably worthy of
our attention.Comment: 7 pages, 3 figure
Leveraging Citation Networks to Visualize Scholarly Influence Over Time
Assessing the influence of a scholar's work is an important task for funding
organizations, academic departments, and researchers. Common methods, such as
measures of citation counts, can ignore much of the nuance and
multidimensionality of scholarly influence. We present an approach for
generating dynamic visualizations of scholars' careers. This approach uses an
animated node-link diagram showing the citation network accumulated around the
researcher over the course of the career in concert with key indicators,
highlighting influence both within and across fields. We developed our design
in collaboration with one funding organization---the Pew Biomedical Scholars
program---but the methods are generalizable to visualizations of scholarly
influence. We applied the design method to the Microsoft Academic Graph, which
includes more than 120 million publications. We validate our abstractions
throughout the process through collaboration with the Pew Biomedical Scholars
program officers and summative evaluations with their scholars
- …