Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Regularization Approach for Prediction of Edges and Node Features in
  Dynamic Graphs   We consider the two problems of predicting links in a dynamic graph sequence
and predicting functions defined at each node of the graph. In many
applications, the solution of one problem is useful for solving the other.
Indeed, if these functions reflect node features, then they are related through
the graph structure. In this paper, we formulate a hybrid approach that
simultaneously learns the structure of the graph and predicts the values of the
node-related functions. Our approach is based on the optimization of a joint
regularization objective. We empirically test the benefits of the proposed
method with both synthetic and real data. The results indicate that joint
regularization improves prediction performance over the graph evolution and the
node features.

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The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
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The categories of this paper are: econ
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The categories of this paper are: q-bio
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The categories of this paper are: hep-lat
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The categories of this paper are: dg-ga
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The categories of this paper are: quant-ph
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The categories of this paper are: q-fin
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-th
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The categories of this paper are: chem-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ex
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The categories of this paper are: solv-int
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The categories of this paper are: bayes-an