Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Supervised learning on heterogeneous, attributed entities interacting
  over time   Most physical or social phenomena can be represented by ontologies where the
constituent entities are interacting in various ways with each other and with
their environment. Furthermore, those entities are likely heterogeneous and
attributed with features that evolve dynamically in time as a response to their
successive interactions. In order to apply machine learning on such entities,
e.g., for classification purposes, one therefore needs to integrate the
interactions into the feature engineering in a systematic way. This proposal
shows how, to this end, the current state of graph machine learning remains
inadequate and needs to be be augmented with a comprehensive feature
engineering paradigm in space and time.

29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph