Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Interpretable Signed Link Prediction with Signed Infomax Hyperbolic
  Graph   Signed link prediction in social networks aims to reveal the underlying
relationships (i.e. links) among users (i.e. nodes) given their existing
positive and negative interactions observed. Most of the prior efforts are
devoted to learning node embeddings with graph neural networks (GNNs), which
preserve the signed network topology by message-passing along edges to
facilitate the downstream link prediction task. Nevertheless, the existing
graph-based approaches could hardly provide human-intelligible explanations for
the following three questions: (1) which neighbors to aggregate, (2) which path
to propagate along, and (3) which social theory to follow in the learning
process. To answer the aforementioned questions, in this paper, we investigate
how to reconcile the \textit{balance} and \textit{status} social rules with
information theory and develop a unified framework, termed as Signed Infomax
Hyperbolic Graph (\textbf{SIHG}). By maximizing the mutual information between
edge polarities and node embeddings, one can identify the most representative
neighboring nodes that support the inference of edge sign. Different from
existing GNNs that could only group features of friends in the subspace, the
proposed SIHG incorporates the signed attention module, which is also capable
of pushing hostile users far away from each other to preserve the geometry of
antagonism. The polarity of the learned edge attention maps, in turn, provide
interpretations of the social theories used in each aggregation. In order to
model high-order user relations and complex hierarchies, the node embeddings
are projected and measured in a hyperbolic space with a lower distortion.
Extensive experiments on four signed network benchmarks demonstrate that the
proposed SIHG framework significantly outperforms the state-of-the-arts in
signed link prediction.

24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
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The categories of this paper are: solv-int
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The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con