Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Community Detection   A deep community in a graph is a connected component that can only be seen
after removal of nodes or edges from the rest of the graph. This paper
formulates the problem of detecting deep communities as multi-stage node
removal that maximizes a new centrality measure, called the local Fiedler
vector centrality (LFVC), at each stage. The LFVC is associated with the
sensitivity of algebraic connectivity to node or edge removals. We prove that a
greedy node/edge removal strategy, based on successive maximization of LFVC,
has bounded performance loss relative to the optimal, but intractable,
combinatorial batch removal strategy. Under a stochastic block model framework,
we show that the greedy LFVC strategy can extract deep communities with
probability one as the number of observations becomes large. We apply the
greedy LFVC strategy to real-world social network datasets. Compared with
conventional community detection methods we demonstrate improved ability to
identify important communities and key members in the network.

22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci