  Signed network embedding is an approach to learn low-dimensional
representations of nodes in signed networks with both positive and negative
links, which facilitates downstream tasks such as link prediction with general
data mining frameworks. Due to the distinct properties and significant added
value of negative links, existing signed network embedding methods usually
design dedicated methods based on social theories such as balance theory and
status theory. However, existing signed network embedding methods ignore the
characteristics of multiple facets of each node and mix them up in one single
representation, which limits the ability to capture the fine-grained attentions
between node pairs. In this paper, we propose MUSE, a MUlti-faceted
attention-based Signed network Embedding framework to tackle this problem.
Specifically, a joint intra- and inter-facet attention mechanism is introduced
to aggregate fine-grained information from neighbor nodes. Moreover, balance
theory is also utilized to guide information aggregation from multi-order
balanced and unbalanced neighbors. Experimental results on four real-world
signed network datasets demonstrate the effectiveness of our proposed
framework.
