Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adapting Stochastic Block Models to Power-Law Degree Distributions   Stochastic block models (SBMs) have been playing an important role in
modeling clusters or community structures of network data. But, it is incapable
of handling several complex features ubiquitously exhibited in real-world
networks, one of which is the power-law degree characteristic. To this end, we
propose a new variant of SBM, termed power-law degree SBM (PLD-SBM), by
introducing degree decay variables to explicitly encode the varying degree
distribution over all nodes. With an exponential prior, it is proved that
PLD-SBM approximately preserves the scale-free feature in real networks. In
addition, from the inference of variational E-Step, PLD-SBM is indeed to
correct the bias inherited in SBM with the introduced degree decay factors.
Furthermore, experiments conducted on both synthetic networks and two
real-world datasets including Adolescent Health Data and the political blogs
network verify the effectiveness of the proposed model in terms of cluster
prediction accuracies.

23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci