Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semisupervised Learning on Heterogeneous Graphs and its Applications to
  Facebook News Feed   Graph-based semi-supervised learning is a fundamental machine learning
problem, and has been well studied. Most studies focus on homogeneous networks
(e.g. citation network, friend network). In the present paper, we propose the
Heterogeneous Embedding Label Propagation (HELP) algorithm, a graph-based
semi-supervised deep learning algorithm, for graphs that are characterized by
heterogeneous node types. Empirically, we demonstrate the effectiveness of this
method in domain classification tasks with Facebook user-domain interaction
graph, and compare the performance of the proposed HELP algorithm with the
state of the art algorithms. We show that the HELP algorithm improves the
predictive performance across multiple tasks, together with semantically
meaningful embedding that are discriminative for downstream classification or
regression tasks.

12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int