Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-supervised learning in unbalanced and heterogeneous networks   Community detection was a hot topic on network analysis, where the main aim
is to perform unsupervised learning or clustering in networks. Recently,
semi-supervised learning has received increasing attention among researchers.
In this paper, we propose a new algorithm, called weighted inverse Laplacian
(WIL), for predicting labels in partially labeled networks. The idea comes from
the first hitting time in random walk, and it also has nice explanations both
in information propagation and the regularization framework. We propose a
partially labeled degree-corrected block model (pDCBM) to describe the
generation of partially labeled networks. We show that WIL ensures the
misclassification rate is of order $O(\frac{1}{d})$ for the pDCBM with average
degree $d=\Omega(\log n),$ and that it can handle situations with greater
unbalanced than traditional Laplacian methods. WIL outperforms other
state-of-the-art methods in most of our simulations and real datasets,
especially in unbalanced networks and heterogeneous networks.

22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an