Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neighbor2vec: an efficient and effective method for Graph Embedding   Graph embedding techniques have led to significant progress in recent years.
However, present techniques are not effective enough to capture the patterns of
networks. This paper propose neighbor2vec, a neighbor-based sampling strategy
used algorithm to learn the neighborhood representations of node, a framework
to gather the structure information by feature propagation between the node and
its neighbors. We claim that neighbor2vec is a simple and effective approach to
enhancing the scalability as well as equality of graph embedding, and it breaks
the limits of the existing state-of-the-art unsupervised techniques. We conduct
experiments on several node classification and link prediction tasks for
networks such as ogbn-arxiv, ogbn-products, ogbn-proteins, ogbl-ppa,ogbl-collab
and ogbl-citation2. The result shows that Neighbor2vec's representations
provide an average accuracy scores up to 6.8 percent higher than competing
methods in node classification tasks and 3.0 percent higher in link prediction
tasks. The neighbor2vec's representations are able to outperform all baseline
methods and two classical GNN models in all six experiments.

10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex