2014/06/30 by Darko Hric, Richard K. Darst, Santo Fortunato +1 · 6 citations
Biochemistry, Genetics and Molecular Biology · Business, Management and Accounting · Computer Science · Environmental Science · Mathematics · Physics and Astronomy · Psychology · Social Sciences · #Artificial intelligence #Behavioral Health and Interventions #Cognitive psychology #Combinatorics #Community structure #Complex Network Analysis Techniques #Computer science #Data mining #Environmental Education and Sustainability #Environmental Sustainability in Business #Goal orientation #Goal setting #Ground truth #Human Mobility and Location-Based Analysis #Information retrieval #Mathematics #Metadata #Opinion Dynamics and Social Influence #Psychology #Social psychology #Theoretical computer science #Topology (electrical circuits) #World Wide Web #cs.IR #cs.SI #physics.soc-ph #q-bio.QM
paper · pdf · doi:10.1103/physreve.90.062805
published in Physical Review E 90(6), 062805 (American Physical Society) · 21 pages, 19 figures
arxiv created 2014/12/11 · arxiv updated 2014/12/12 · openalex publication_date 2015/02/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23
Algorithms to find communities in networks rely just on structural information and search for cohesive subsets of nodes. On the other hand, most scholars implicitly or explicitly assume that structural communities represent groups of nodes with similar (nontopological) properties or functions. This hypothesis could not be verified, so far, because of the lack of network datasets with information on the classification of the nodes. We show that traditional community detection methods fail to find the metadata groups in many large networks. Our results show that there is a marked separation between structural communities and metadata groups, in line with recent findings. That means that either our current modeling of community structure has to be substantially modified, or that metadata groups may not be recoverable from topology alone.