Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Visual Detection of Structural Changes in Time-Varying Graphs Using
  Persistent Homology   Topological data analysis is an emerging area in exploratory data analysis
and data mining. Its main tool, persistent homology, has become a popular
technique to study the structure of complex, high-dimensional data. In this
paper, we propose a novel method using persistent homology to quantify
structural changes in time-varying graphs. Specifically, we transform each
instance of the time-varying graph into metric spaces, extract topological
features using persistent homology, and compare those features over time. We
provide a visualization that assists in time-varying graph exploration and
helps to identify patterns of behavior within the data. To validate our
approach, we conduct several case studies on real world data sets and show how
our method can find cyclic patterns, deviations from those patterns, and
one-time events in time-varying graphs. We also examine whether
persistence-based similarity measure as a graph metric satisfies a set of
well-established, desirable properties for graph metrics.

25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph