  Network visualization has established as a key complement to network analysis
since the large variety of existing network layouts are able to graphically
highlight different properties of networks. However, signed networks, i.e.,
networks whose edges are labeled as friendly (positive) or antagonistic
(negative), are target of few of such layouts and none, to our knowledge, is
able to show structural balance, i.e., the tendency of cycles towards including
an even number of negative edges, which is a well-known theory for studying
friction and polarization.
  In this work we present Structural-balance-viz: a novel visualization method
showing whether a connected signed network is balanced or not and, in the
latter case, how close the network is to be balanced. Structural-balance-viz
exploits spectral computations of the signed Laplacian matrix to place
network's nodes in a Cartesian coordinate system resembling a balance (a
scale). Moreover, it uses edge coloring and bundling to distinguish positive
and negative interactions. The proposed visualization method has
characteristics desirable in a variety of network analysis tasks:
Structural-balance-viz is able to provide indications of balance/polarization
of the whole network and of each node, to identify two factions of nodes on the
basis of their polarization, and to show their cumulative characteristics.
Moreover, the layout is reproducible and easy to compare.
Structural-balance-viz is validated over synthetic-generated networks and
applied to a real-world dataset about political debates confirming that it is
able to provide meaningful interpretations.
