  Signed networks allow us to model conflicting relationships and interactions,
such as friend/enemy and support/oppose. These signed interactions happen in
real-time. Modeling such dynamics of signed networks is crucial to
understanding the evolution of polarization in the network and enabling
effective prediction of the signed structure (i.e., link signs and signed
weights) in the future. However, existing works have modeled either (static)
signed networks or dynamic (unsigned) networks but not dynamic signed networks.
Since both sign and dynamics inform the graph structure in different ways, it
is non-trivial to model how to combine the two features. In this work, we
propose a new Graph Neural Network (GNN)-based approach to model dynamic signed
networks, named SEMBA: Signed link's Evolution using Memory modules and
Balanced Aggregation. Here, the idea is to incorporate the signs of temporal
interactions using separate modules guided by balance theory and to evolve the
embeddings from a higher-order neighborhood. Experiments on 4 real-world
datasets and 4 different tasks demonstrate that SEMBA consistently and
significantly outperforms the baselines by up to $80\%$ on the tasks of
predicting signs of future links while matching the state-of-the-art
performance on predicting the existence of these links in the future. We find
that this improvement is due specifically to the superior performance of SEMBA
on the minority negative class.
