Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-centrality Graph Spectral Decompositions and their Application to
  Cyber Intrusion Detection   Many modern datasets can be represented as graphs and hence spectral
decompositions such as graph principal component analysis (PCA) can be useful.
Distinct from previous graph decomposition approaches based on subspace
projection of a single topological feature, e.g., the Fiedler vector of
centered graph adjacency matrix (graph Laplacian), we propose spectral
decomposition approaches to graph PCA and graph dictionary learning that
integrate multiple features, including graph walk statistics, centrality
measures and graph distances to reference nodes. In this paper we propose a new
PCA method for single graph analysis, called multi-centrality graph PCA
(MC-GPCA), and a new dictionary learning method for ensembles of graphs, called
multi-centrality graph dictionary learning (MC-GDL), both based on spectral
decomposition of multi-centrality matrices. As an application to cyber
intrusion detection, MC-GPCA can be an effective indicator of anomalous
connectivity pattern and MC-GDL can provide discriminative basis for attack
classification.

18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex