2016/08/20 by Leto Peel, Daniel B. Larremore, Aaron Clauset · 1 voice · 9 citations
Computer Science · Mathematics · Physics and Astronomy · #Artificial intelligence #Class (philosophy) #Community structure #Complex Network Analysis Techniques #Computer science #Data Visualization and Analytics #Data mining #Data science #Granularity #Ground truth #Information retrieval #Machine learning #Mathematics #Metadata #Node (physics) #Opinion Dynamics and Social Influence #Theoretical computer science #World Wide Web #cs.SI #physics.data-an #physics.soc-ph #stat.ML
paper · pdf · doi:10.1126/sciadv.1602548
published as Science Advances 3(5) e1602548, 2017 · 27 pages, 10 figures, 11 tables
arxiv published 2016/08/20 · arxiv created 2017/05/03 · arxiv updated 2017/05/03 · openalex publication_date 2017/05/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Across many scientific domains, there is a common need to automatically extract a simplified view or coarse-graining of how a complex system's components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate the performance of community detection algorithms by their ability to find so-called "ground truth" communities. This works well in synthetic networks with planted communities because such networks' links are formed explicitly based on those known communities. However, there are no planted communities in real world networks. Instead, it is standard practice to treat some observed discrete-valued node attributes, or metadata, as ground truth. Here, we show that metadata are not the same as ground truth, and that treating them as such induces severe theoretical and practical problems. We prove that no algorithm can uniquely solve community detection, and we prove a general No Free Lunch theorem for community detection, which implies that there can be no algorithm that is optimal for all possible community detection tasks. However, community detection remains a powerful tool and node metadata still have value so a careful exploration of their relationship with network structure can yield insights of genuine worth. We illustrate this point by introducing two statistical techniques that can quantify the relationship between metadata and community structure for a broad class of models. We demonstrate these techniques using both synthetic and real-world networks, and for multiple types of metadata and community structure.