Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: HYPA: Efficient Detection of Path Anomalies in Time Series Data on
  Networks   The unsupervised detection of anomalies in time series data has important
applications in user behavioral modeling, fraud detection, and cybersecurity.
Anomaly detection has, in fact, been extensively studied in categorical
sequences. However, we often have access to time series data that represent
paths through networks. Examples include transaction sequences in financial
networks, click streams of users in networks of cross-referenced documents, or
travel itineraries in transportation networks. To reliably detect anomalies, we
must account for the fact that such data contain a large number of independent
observations of paths constrained by a graph topology. Moreover, the
heterogeneity of real systems rules out frequency-based anomaly detection
techniques, which do not account for highly skewed edge and degree statistics.
To address this problem, we introduce HYPA, a novel framework for the
unsupervised detection of anomalies in large corpora of variable-length
temporal paths in a graph. HYPA provides an efficient analytical method to
detect paths with anomalous frequencies that result from nodes being traversed
in unexpected chronological order.

22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org