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.

翻译：决策过程往往涉及投票。人类与立法、产品等外生实体之间的互动，可以有效地建模为双模（二部）符号网络——其中人们可以对实体投赞成票、反对票或弃权。检测此类网络中的社区，有助于我们理解潜在特性，例如意识形态阵营或消费者偏好。尽管社区检测在二部网络和符号网络中已分别建立成熟实践，但在双模符号网络情形下仍基本未被探索。本文通过综合基准测试和真实数据集，系统评估了双模符号网络中社区检测方法的有效性。研究发现：当数据中不存在社区时，这些方法常会恢复出虚假社区；而当存在社区时，算法展现了良好的性能，但其表现对参数选择高度敏感。这表明，在双模符号网络中使用社区检测方法的研究者不应将所发现的社区视为当然结果——评估参数选择的稳健性或进行领域特定的外部验证至关重要。