Finding semantic communities using network topology and contents together is a hot topic in community detection. Existing methods often use word attributes in an indiscriminate way to help finding communities. Through analysis we find that, words in networked contents often embody a hierarchical semantic structure. Some words reflect a background topic of the whole network with all communities, some imply the high-level general topic covering several topic-related communities, and some imply the high-resolution specialized topic to describe each community. Ignoring such semantic structures often leads to defects in depicting networked contents where deep semantics are not fully utilized. To solve this problem, we propose a new Bayesian probabilistic model. By distinguishing words from either a background topic or some two-level topics (i.e., general and specialized topics), this model not only better utilizes the networked contents to help finding communities, but also provides a clearer multiplex semantic community interpretation. We then give an efficient variational algorithm for model inference. The superiority of this new approach is demonstrated by comparing with ten state-of-the-art methods on nine real networks and an artificial benchmark. A case study is further provided to show its strong ability in deep semantic interpretation of communities.

中文翻译：

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通过区分背景、一般和专业主题来检测具有多重语义的社区

使用网络拓扑和内容一起寻找语义社区是社区检测中的一个热门话题。现有方法经常不加选择地使用词属性来帮助寻找社区。通过分析我们发现，网络内容中的词往往体现了层次化的语义结构。有的词反映了全网所有社区的背景话题，有的暗示了涵盖多个与话题相关的社区的高级通用话题，有的则暗示了描述每个社区的高分辨率专业话题。忽略这种语义结构通常会导致在深度语义未被充分利用的情况下描绘网络内容的缺陷。为了解决这个问题，我们提出了一种新的贝叶斯概率模型。通过将单词与背景主题或某些两级主题（即，一般和专业主题），该模型不仅更好地利用网络内容来帮助寻找社区，而且还提供了更清晰的多元语义社区解释。然后，我们给出了一种用于模型推理的有效变分算法。通过与 9 个真实网络上的 10 种最先进方法和人工基准进行比较，证明了这种新方法的优越性。进一步提供了一个案例研究，以展示其在社区深度语义解释方面的强大能力。通过与 9 个真实网络上的 10 种最先进方法和人工基准进行比较，证明了这种新方法的优越性。进一步提供了一个案例研究，以展示其在社区深度语义解释方面的强大能力。通过与 9 个真实网络上的 10 种最先进方法和人工基准进行比较，证明了这种新方法的优越性。进一步提供了一个案例研究，以展示其在社区深度语义解释方面的强大能力。