Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify
  Scientific Impact   Quantifying and predicting the long-term impact of scientific writings or
individual scholars has important implications for many policy decisions, such
as funding proposal evaluation and identifying emerging research fields. In
this work, we propose an approach based on Heterogeneous Dynamical Graph Neural
Network (HDGNN) to explicitly model and predict the cumulative impact of papers
and authors. HDGNN extends heterogeneous GNNs by incorporating temporally
evolving characteristics and capturing both structural properties of attributed
graph and the growing sequence of citation behavior. HDGNN is significantly
different from previous models in its capability of modeling the node impact in
a dynamic manner while taking into account the complex relations among nodes.
Experiments conducted on a real citation dataset demonstrate its superior
performance of predicting the impact of both papers and authors.

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The categories of this paper are: funct-an
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th