We investigate how well traditional fiction genres like Fantasy, Thriller, and Literature represent readers' preferences. Using user data from Goodreads we construct a book network where two books are strongly linked if the same people tend to read or enjoy them both. We then partition this network into communities of similar books and assign each a list of subjects from The Open Library to serve as a proxy for traditional genres. Our analysis reveals that the network communities correspond to existing combinations of traditional genres, but that the exact communities differ depending on whether we consider books that people read or books that people enjoy. In addition, we apply principal component analysis to the data and find that the variance in the book communities is best explained by two factors: the maturity/childishness and realism/fantastical nature of the books. We propose using this maturity-realism plane as a coarse classification tool for stories.

翻译：我们探讨了传统小说类型（如奇幻、惊悚与文学作品）在多大程度上能反映读者偏好。基于Goodreads用户数据，我们构建了一个图书网络：若两本书被同一人群阅读或喜爱，则它们之间具有强关联。随后，我们将该网络划分为图书社区（相似图书聚类），并为每个社区匹配《开放图书馆》（Open Library）中的主题标签作为传统类型的代理指标。分析表明，网络社区与传统类型的现有组合存在对应关系，但具体社区结构会因选取“读者阅读的书籍”或“读者喜爱的书籍”而呈现差异。此外，通过主成分分析发现，图书社区差异主要由两个因子解释：作品的成熟度/幼稚性与写实性/奇幻性。我们提出将这一“成熟-写实”二维平面作为故事的新型粗粒度分类工具。