Community detection and social network analysis based on the Italian wars of the 15th century

Fumanal-Idocin, Javier; Alonso‐Betanzos, Amparo; Cordón, Óscar; Bustince, Humberto; Minárová, Mária

doi:10.1016/j.future.2020.06.030

Cited by 22 publications

(18 citation statements)

References 39 publications

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“…For the sake of comparison, we have also compared the results obtained with these algorithms with those obtained by the Borgia Clustering [21]. The Borgia Clustering is a community detection algorithm that generalizes the gravitational clustering algorithm [22] using affinity functions, among other changes.…”

Section: Community Detection Algorithmsmentioning

confidence: 99%

“…Another proposal that does not include a modularity optimization process is [21], in which the authors propose to use a new kind of functions, the affinity functions, that model the local interactions between actors according to different social behaviours. They do so in order to propose a community detection algorithm, the Borgia Clustering, that simulates the dynamics of the conquests of Cesare Borgia in the Italian Renaissance.…”

Section: Introductionmentioning

confidence: 99%

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Combinations of Affinity Functions for Different Community Detection Algorithms in Social Networks

Idocin¹,

Cordón²,

Minárová³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Social network analysis is a popular discipline among the social and behavioural sciences, in which the relationships between different social entities are modelled as a network.One of the most popular problems in social network analysis is finding communities in its network structure.Usually, a community in a social network is a functional sub-partition of the graph. However, as the definition of community is somewhat imprecise, many algorithms have been proposed to solve this task, each of them focusing on different social characteristics of the actors and the communities. In this work we propose to use novel combinations of affinity functions, which are designed to capture different social mechanics in the network interactions. We use them to extend already existing community detection algorithms in order to combine the capacity of the affinity functions to model different social interactions than those exploited by the original algorithms.

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Section: Community Detection Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combinations of Affinity Functions for Different Community Detection Algorithms in Social Networks

Idocin¹,

Cordón²,

Minárová³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

“…Numerous researches have examined the types of centrality criteria which can be studied in [14]. In [14], using the concept of centrality (degree centrality and closeness centrality), the performance of social networks has been analyzed [15,16].…”

Section: -Related Workmentioning

confidence: 99%

Improved Leach algorithm for detecting opinion leaders in social networks

Elmi¹

2020

Preprint

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<p>The most important features of the proposed method include: 1) introducing an innovation in Leach algorithm and applying it in a different field from its common application, 2) using standard centrality indicators to calculate the correlation degree in order to improve the identification of opinion leaders in social networks, 3)this algorithm is completely automatic and works without operator intervention.</p>

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“…This example is used to discredit the need to impose the triangle inequality in human reasoning, as well as to evince the multidimensional nature of the human interpretation. It also serves as an illustration of the interesting role of the notion of context in human comparisons [14].…”

Section: Introductionmentioning

confidence: 99%

On the Role of Context-Awareness in Binary Image Comparison

Iglesias-Rey¹,

Castillo-Lopez²,

López-Molina³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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The quantification of image similarity has been a key topic in the computer vision literature for the past few years. Different mathematical theories have been used in the development of these measures, which we will refer to as comparison measures. An interesting aspect in the study of comparison measures is the natural requirement to replicate human behavior. In almost all cases, it is appropriate for a comparison measure to produce results that are consistent with how humans would perform that assessment. However, despite accepting this premise, most of the proposals in the literature ignore a fundamental characteristic of the way in which humans carry out this evaluation: the context of comparison. In this work we present a comparison measure for binary images that incorporates the context of comparison; more precisely, we introduce an approach for the generation of ultrametrics for the context-aware comparison of binary images.

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Community detection and social network analysis based on the Italian wars of the 15th century

Cited by 22 publications

References 39 publications

Combinations of Affinity Functions for Different Community Detection Algorithms in Social Networks

Combinations of Affinity Functions for Different Community Detection Algorithms in Social Networks

Improved Leach algorithm for detecting opinion leaders in social networks

On the Role of Context-Awareness in Binary Image Comparison

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