https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI/Head https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI/assertion https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI/provenance https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI/pubinfo https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI/assertion https://ieeexplore.ieee.org/abstract/document/10903272 http://purl.org/dc/terms/creator https://orcid.org/0000-0001-8726-8226 https://ieeexplore.ieee.org/abstract/document/10903272 http://purl.org/dc/terms/creator https://orcid.org/0000-0001-9487-5622 https://ieeexplore.ieee.org/abstract/document/10903272 http://purl.org/dc/terms/creator https://orcid.org/0000-0002-3588-6257 https://ieeexplore.ieee.org/abstract/document/10903272 http://purl.org/dc/terms/publisher https://ror.org/01n002310 https://ieeexplore.ieee.org/abstract/document/10903272 http://purl.org/dc/terms/subject http://edamontology.org/topic_3316 https://ieeexplore.ieee.org/abstract/document/10903272 http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/fair/ff/terms/article https://ieeexplore.ieee.org/abstract/document/10903272 http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/fdof/ontology#FAIRDigitalObject https://ieeexplore.ieee.org/abstract/document/10903272 http://www.w3.org/2000/01/rdf-schema#comment Community detection plays a pivotal role in social network analysis by partitioning networks into cohesive groups of vertices with dense intra-group connections and sparse intergroup connections. In this paper, we utilized a scholarly social network based on researchers’ topic similarity derived from their publication metadata to identify interdisciplinary research communities. As topics often form a hierarchy, we hypothesize that the constructed scholarly network will exhibit hierarchical community structures. Therefore, we explore the efficacy of two prominent community detection algorithms, Louvain and Spectral clustering, known for their capacity to detect hierarchical community structures within networks. While both algorithms demonstrate this capability, the original Louvain algorithm is susceptible to the resolution limit problem due to its reliance on the modularity measure. To address this limitation, we propose the nested hierarchical Louvain algorithm, which iteratively partitions the network based on previously identified subgraphs, and we find that the bias towards large communities is mitigated. To evaluate the hierarchy produced by each of the algorithms, we employ the Cophenetic Correlation Coefficient (CPCC), a metric commonly used in hierarchical clustering evaluations but less frequently utilized in hierarchical community analysis. We argue that CPCC can be a useful measure to identify the presence of implicit hierarchical community structure in social networks when it is not explicitly available from domain knowledge while also further mitigating the inherent bias present in using modularity as a metric. Experimental results, conducted on both synthetic networks and the scholarly social network, demonstrate that the nested hierarchical Louvain algorithm, as well as Spectral Clustering, successfully identifies more finely structured hierarchical communities, offering greater depth in the dendrogram compared to the basic Louvain algorithm. Index Terms—Social Networks, Hierarchical Community Detection, Clustering, Topic Models https://ieeexplore.ieee.org/abstract/document/10903272 http://www.w3.org/2000/01/rdf-schema#label Identifying Hierarchical Community Structures in Content-Based Scholarly Social Networks https://ieeexplore.ieee.org/abstract/document/10903272 https://schema.org/funder https://ror.org/021nxhr62 https://ieeexplore.ieee.org/abstract/document/10903272 https://w3id.org/fdof/ontology#hasMetadata https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI https://ieeexplore.ieee.org/abstract/document/10903272 https://www.w3.org/ns/dcat#contactPoint john.sheppard@montana.edu https://ieeexplore.ieee.org/abstract/document/10903272 https://www.w3.org/ns/dcat#endDate 2025-03-04 https://ieeexplore.ieee.org/abstract/document/10903272 https://www.w3.org/ns/dcat#startDate 2023 https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI/provenance https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI/assertion http://www.w3.org/ns/prov#wasAttributedTo https://orcid.org/0009-0008-8411-2742 https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI/pubinfo https://orcid.org/0009-0008-8411-2742 http://xmlns.com/foaf/0.1/name Emily Regalado https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI http://purl.org/dc/terms/created 2026-02-04T18:00:04.226Z https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI http://purl.org/dc/terms/creator https://orcid.org/0009-0008-8411-2742 https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI http://purl.org/dc/terms/license https://creativecommons.org/licenses/by/4.0/ https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI http://purl.org/nanopub/x/introduces https://ieeexplore.ieee.org/abstract/document/10903272 https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI http://purl.org/nanopub/x/wasCreatedAt https://nanodash.knowledgepixels.com/ https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI https://w3id.org/np/o/ntemplate/wasCreatedFromProvenanceTemplate https://w3id.org/np/RA7lSq6MuK_TIC6JMSHvLtee3lpLoZDOqLJCLXevnrPoU https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI https://w3id.org/np/o/ntemplate/wasCreatedFromPubinfoTemplate https://w3id.org/np/RA0J4vUn_dekg-U1kK3AOEt02p9mT2WO03uGxLDec1jLw https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI https://w3id.org/np/o/ntemplate/wasCreatedFromPubinfoTemplate https://w3id.org/np/RAukAcWHRDlkqxk7H2XNSegc1WnHI569INvNr-xdptDGI https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI https://w3id.org/np/o/ntemplate/wasCreatedFromTemplate https://w3id.org/np/RArM5GTwgxg9qslGX-XiQ-KTTUwdoM0KB1YqmT4GqTizA https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI/sig http://purl.org/nanopub/x/hasAlgorithm RSA https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI/sig http://purl.org/nanopub/x/hasPublicKey MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAxzr6UBGMW6c8tegz0babaledWUEQ0PLDE4tp7Iinbe2DZtAtY5JUptKYuStWDZx+QER4808P8dejNWRnBDzgthYJm/AyNSXflHSJhz2+NC+h7RylOLxbwLEQocmyKKiYxa2gT85m6ajVL2M6TnfG67nnK+K2f7iCGL6wYXRITD1q+7+5SWqBdDXIV921W4IKWaD2GJk+NRBoOqQhbsrk8Tn5XsNd7DMYVHk47oMDGbeBnrOIoRPsbBgAcoCsxxhiB9yN6Lf8EUbnlXVEDzJuZk048L1BDZL+6nkA8btTQGP2ijUFWA7rTrod3LjUDQWLZS95njjl867dtmv/znYkzwIDAQAB https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI/sig http://purl.org/nanopub/x/hasSignature fkK51Bxk/kpAi/xzeR+/jziwQtmabO5O7eMYmxjtxqSz+V6XiBCVAKMTrJKIWjHaoG4bdMip97ywI2MBOwJI5V5J04gWqzaOHvZR5l8LeUYF+rMvkQRksam9GpTi5PWj4pxOKCpIvv693qmlz/8Tnon2+gJl7ABgEkNSzqoGLX/53PLo0btDV44o3coQ8567PeXwpyYTPZufJWtDCCa5byTrSPef2TFlCDSfFBotKbD0xWPxKWodss7fLHVvWH1Bh4KXDswYt9Yi34M9jLXX941H0T+S0eweZYR/4BVFn1cnHma03DmEKJiV07ka2wRQU9uKBYRt//j/q79bcvykig== https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI/sig http://purl.org/nanopub/x/hasSignatureTarget https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI/sig http://purl.org/nanopub/x/signedBy https://orcid.org/0009-0008-8411-2742