Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Size-Consistent Statistics for Anomaly Detection in Dynamic Networks   An important task in network analysis is the detection of anomalous events in
a network time series. These events could merely be times of interest in the
network timeline or they could be examples of malicious activity or network
malfunction. Hypothesis testing using network statistics to summarize the
behavior of the network provides a robust framework for the anomaly detection
decision process. Unfortunately, choosing network statistics that are dependent
on confounding factors like the total number of nodes or edges can lead to
incorrect conclusions (e.g., false positives and false negatives). In this
dissertation we describe the challenges that face anomaly detection in dynamic
network streams regarding confounding factors. We also provide two solutions to
avoiding error due to confounding factors: the first is a randomization testing
method that controls for confounding factors, and the second is a set of
size-consistent network statistics which avoid confounding due to the most
common factors, edge count and node count.

37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph