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Characterizing the Interactions Between Classical and Community-aware Centrality Measures in Complex Networks
Social and Information Networks · Submitted 15 May 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Results indicate that the stronger the community structure, the more appropriate the community-aware centrality measures.
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The abstract, sentence by sentence
Identifying vital nodes in networks exhibiting a community structure is a fundamental issue. Indeed, community structure is one of the main properties of real-world networks. Recent works have shown that community-aware centrality measures compare favorably with classical measures agnostic about this ubiquitous property. Nonetheless, there is no clear consensus about how they relate and in which situation it is better to use a classical or a community-aware centrality measure. To this end, in this paper, we perform an extensive investigation to get a better understanding of the relationship between classical and community-aware centrality measures reported in the literature. Experiments use artificial networks with controlled community structure properties and a large sample of real-world networks originating from various domains. Results indicate that the stronger the community structure, the more appropriate the community-aware centrality measures. Furthermore, variations of the degree and community size distribution parameters do not affect the results. Finally, network transitivity and community structure strength are the most significant drivers controlling the interactions between classical and community-aware centrality measures.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Identifying vital nodes in networks exhibiting a community structure is a fundamental issue. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Indeed, community structure is one of the main properties of real-world networks. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s2 | Recent works have shown that community-aware centrality measures compare favorably with classical measures agnostic about this ubiquitous property. | background 1.00 | 0.04 | 0.04 | asserted 0.99 |
| s3 | Nonetheless, there is no clear consensus about how they relate and in which situation it is better to use a classical or a community-aware centrality measure. | gap 1.00 | 0.04 | 0.02 | asserted 0.99 |
| s4 | To this end, in this paper, we perform an extensive investigation to get a better understanding of the relationship between classical and community-aware centrality measures reported in the literature. | contribution 0.99 | 0.84 | 0.03 | asserted 0.67 |
| s5 | Experiments use artificial networks with controlled community structure properties and a large sample of real-world networks originating from various domains. | method 1.00 | 0.88 | 0.02 | asserted 0.94 |
| s6 | Results indicate that the stronger the community structure, the more appropriate the community-aware centrality measures. | result 1.00 | 0.93 | 0.63 | hedged possibility 0.53 |
| s7 | Furthermore, variations of the degree and community size distribution parameters do not affect the results. | result 1.00 | 0.92 | 0.07 | asserted 1.00 |
| s8 | Finally, network transitivity and community structure strength are the most significant drivers controlling the interactions between classical and community-aware centrality measures. | result 0.92 | 0.90 | 0.48 | asserted 1.00 |
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Original abstract
Identifying vital nodes in networks exhibiting a community structure is a fundamental issue. Indeed, community structure is one of the main properties of real-world networks. Recent works have shown that community-aware centrality measures compare favorably with classical measures agnostic about this ubiquitous property. Nonetheless, there is no clear consensus about how they relate and in which situation it is better to use a classical or a community-aware centrality measure. To this end, in this paper, we perform an extensive investigation to get a better understanding of the relationship between classical and community-aware centrality measures reported in the literature. Experiments use artificial networks with controlled community structure properties and a large sample of real-world networks originating from various domains. Results indicate that the stronger the community structure, the more appropriate the community-aware centrality measures. Furthermore, variations of the degree and community size distribution parameters do not affect the results. Finally, network transitivity and community structure strength are the most significant drivers controlling the interactions between classical and community-aware centrality measures.
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Extensive experiments are performed on a set of 50 real-world networks originating from various fields (social, biological, infrastructural, collaboration, etc.) [ref] .
Handbook of Network Analysis [KONECT -- the Koblenz Network Collection] · Comparative evaluation using real-world networks · 0.97
Cites as support 1 paper
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These works have shown that incorporating community structure information allows designing more effective centrality measures [ref] .
Identifying Influential Spreaders of Epidemics on Community Networks · Introduction · 0.97
Cites as background 1 paper
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In [ref] , Orman et al. show that the community structure strength significantly affects the average distance, transitivity, and assortativity of the network.
On Accuracy of Community Structure Discovery Algorithms · Introduction · 0.80
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