Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Network Vector: Distributed Representations of Networks with Global
  Context   We propose a neural embedding algorithm called Network Vector, which learns
distributed representations of nodes and the entire networks simultaneously. By
embedding networks in a low-dimensional space, the algorithm allows us to
compare networks in terms of structural similarity and to solve outstanding
predictive problems. Unlike alternative approaches that focus on node level
features, we learn a continuous global vector that captures each node's global
context by maximizing the predictive likelihood of random walk paths in the
network. Our algorithm is scalable to real world graphs with many nodes. We
evaluate our algorithm on datasets from diverse domains, and compare it with
state-of-the-art techniques in node classification, role discovery and concept
analogy tasks. The empirical results show the effectiveness and the efficiency
of our algorithm.

4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
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The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ