Community detection in networks is a fundamental data analysis task. Recently, researchers have tried to improve its performance by exploiting semantic contents and interpret the communities. However, they typically assume that communities are assortative (i.e. vertices are mostly connected to others within the group), thus they cannot find the generalized community structures, which includes assortative communities, disassortative communities (i.e. most connections are from two groups), or a combination. In addition, they often assume that each group membership corresponds to a single topic, thus they cannot perform well when the contents are not consistent with community structures. To address these two issues, we propose a new Bayesian model and develop an efficient variational inference algorithm for model inference. This model describes the generalized communities and the topical clusters separately, and explores their latent correlation simultaneously to make the two parts mutually reinforcing. Our model is not only robust to the above problems, but also can interpret each community using more than one topic. We validate the robustness of this approach on an artificial benchmark, and analyze its interpretability by a case study. We finally show its superior community detection performance by comparing with eight state-of-the-art algorithms on eight real networks.

中文翻译：

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大型社交网络中的广义语义社区的识别

网络中的社区检测是一项基本的数据分析任务。最近，研究人员试图通过利用语义内容和解释社区来提高其性能。但是，他们通常会假设社区是混杂的（即，顶点大多与组中的其他社区相连），因此他们无法找到广义的社区结构，其中包括分类的社区，分散的社区（即，大多数连接来自两个组）或组合。此外，他们经常假设每个组成员资格都对应一个主题，因此，当内容与社区结构不一致时，他们将无法很好地表现。为了解决这两个问题，我们提出了一个新的贝叶斯模型并开发了一种有效的变分推理算法进行模型推理。该模型分别描述了广义社区和主题聚类，并同时探索它们之间的潜在相关性以使两个部分相互增强。我们的模型不仅对上述问题具有鲁棒性，而且可以使用多个主题来解释每个社区。我们在人工基准上验证此方法的鲁棒性，并通过案例研究分析其可解释性。通过与八个真实网络上的八种最新算法进行比较，我们最终展示出其优越的社区检测性能。我们在人工基准上验证此方法的鲁棒性，并通过案例研究分析其可解释性。通过与八个真实网络上的八种最新算法进行比较，我们最终展示出其优越的社区检测性能。我们在人工基准上验证此方法的鲁棒性，并通过案例研究分析其可解释性。通过与八个真实网络上的八种最新算法进行比较，我们最终展示出其优越的社区检测性能。