Nanopublication

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https://w3id.org/np/RACmvAwqmb-z9StuvMLOyUGbEBa8YCAbR0AjAi3DhClEI
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Content

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  <https://ieeexplore.ieee.org/abstract/document/10903272> a <https://w3id.org/fair/ff/terms/article>,
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    dct:creator orcid:0000-0001-8726-8226, orcid:0000-0001-9487-5622, orcid:0000-0002-3588-6257;
    dct:publisher <https://ror.org/01n002310>;
    dct:subject <http://edamontology.org/topic_3316>;
    rdfs: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""";
    rdfs:label "Identifying Hierarchical Community Structures in Content-Based Scholarly Social Networks";
    <https://schema.org/funder> <https://ror.org/021nxhr62>;
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    <https://www.w3.org/ns/dcat#contactPoint> "john.sheppard@montana.edu";
    <https://www.w3.org/ns/dcat#endDate> "2025-03-04";
    <https://www.w3.org/ns/dcat#startDate> "2023" .
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  orcid:0009-0008-8411-2742 foaf:name "Emily Regalado" .
  
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    dct:license <https://creativecommons.org/licenses/by/4.0/>;
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