  Decision-making processes often involve voting. Human interactions with
exogenous entities such as legislations or products can be effectively modeled
as two-mode (bipartite) signed networks-where people can either vote
positively, negatively, or abstain from voting on the entities. Detecting
communities in such networks could help us understand underlying properties:
for example ideological camps or consumer preferences. While community
detection is an established practice separately for bipartite and signed
networks, it remains largely unexplored in the case of bipartite signed
networks. In this paper, we systematically evaluate the efficacy of community
detection methods on bipartite signed networks using a synthetic benchmark and
real-world datasets. Our findings reveal that when no communities are present
in the data, these methods often recover spurious communities. When communities
are present, the algorithms exhibit promising performance, although their
performance is highly susceptible to parameter choice. This indicates that
researchers using community detection methods in the context of bipartite
signed networks should not take the communities found at face value: it is
essential to assess the robustness of parameter choices or perform
domain-specific external validation.
