Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Evaluation of Community Detection Methods   Community structures are critical towards understanding not only the network
topology but also how the network functions. However, how to evaluate the
quality of detected community structures is still challenging and remains
unsolved. The most widely used metric, normalized mutual information (NMI), was
proved to have finite size effect, and its improved form relative normalized
mutual information (rNMI) has reverse finite size effect. Corrected normalized
mutual information (cNMI) was thus proposed and has neither finite size effect
nor reverse finite size effect. However, in this paper we show that cNMI
violates the so-called proportionality assumption. In addition, NMI-type
metrics have the problem of ignoring importance of small communities. Finally,
they cannot be used to evaluate a single community of interest. In this paper,
we map the computed community labels to the ground-truth ones through integer
linear programming, then use kappa index and F-score to evaluate the detected
community structures. Experimental results demonstrate the advantages of our
method.

27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg