Coincidence complex networks

Costa, Luciano da Fontoura

doi:10.1088/2632-072x/ac54c3

Cited by 16 publications

(9 citation statements)

References 31 publications

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“…As described above, the modules extracted from the original document need to be interconnected according to their content similarity. In the present work, we employ the coincidence similarity index [5,6,38] for that finality, in order to perform more selective and sensitive similarity quantification.…”

Section: The Coincidence Similarity Indexmentioning

confidence: 99%

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Quantifying the hierarchical adherence of modular documents

Benatti,

Brito,

Amancio

et al. 2023

J. Phys. Complex.

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Several natural and artificial structures are characterized by an intrinsic hierarchical organization. The present work describes a methodology for quantifying the degree of adherence between a given hierarchical template and a respective modular document (e.g. books or homepages with content organized into modules) organized as a respective content network. The original document, which in the case of the present work concerns Wikipedia pages, is transformed into a respective content network by first dividing the document into parts or modules. Then, the contents (words) of each pair of modules are compared in terms of the coincidence similarity index, yielding a respective weight. The adherence between the hierarchical template and the content network can then be measured by considering the coincidence similarity between the respective adjacency matrices, leading to the respective hierarchical adherence index. In order to provide additional information about this adherence, four specific indices are also proposed, quantifying the number of links between non-adjacent levels, links between nodes in the same level, converging links between adjacent levels, and missing links. The potential of the approach is illustrated respectively to model-theoretical networks as well as to real-world data obtained from Wikipedia. In addition to confirming the effectiveness of the suggested concepts and methods, the results suggest that real-world documents do not tend to substantially adhere to respective hierarchical templates.

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Section: The Coincidence Similarity Indexmentioning

confidence: 99%

“…In order to integrate the complementary characteristics of the two previous indices, the Coincidence similarity index (C) has been described in [5,38,45], corresponding to the product between the Jaccard (equations ( 1) and ( 2)) and Interiority (equation ( 3) and ( 4)) indexes, i.e. :…”

Section: The Coincidence Similarity Indexmentioning

confidence: 99%

Quantifying the hierarchical adherence of modular documents

Benatti,

Brito,

Amancio

et al. 2023

J. Phys. Complex.

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“…The Coincidence similarity Index [10][11][12] has been described as a measure to determine the similarity between virtually any type of mathematical entity, taking into account both the Jaccard and the Interiority (or overlap [21]) indices. This approach is motivated by the relative interiority between the compared sets not being captured by the Jaccard index [10], as well as by the need to generalize the Jaccard index to real-valued structures, including possibly negative values.…”

Section: Basic Conceptsmentioning

confidence: 99%

“…The present work aims to study this interesting and relevant problem using the recently proposed coincidence similarity methodology [10][11][12][13]. In addition, we provide a simple but effective method for generating trees with diverse properties (e.g.…”

Section: Introductionmentioning

confidence: 99%

Recovering hierarchies in terms of content similarity

Benatti¹,

Costa²

2023

J. Phys. A: Math. Theor.

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Several real-world and abstract structures and systems are characterized by marked hierarchy to the point of being expressed as trees. Since the study of these entities often involves sampling (or discovering) the tree nodes in a specific order that may not correspond to the original shape of the tree, reconstruction errors can be obtained. The present work addresses this important problem based on two main resources: (i) the adoption of a simple model of trees, involving a single parameter; and (ii) the use of the coincidence similarity as the means to quantify the errors by comparing the original and reconstructed structures considering the effects of hierarchical structure, nodes content, and uncertainty. Several interesting results are described and discussed, including that the accuracy of hierarchical reconstructions is highly dependent on the values of the uncertainty parameter as well as on the types of trees and that changes in the value of the content parameter can affect the accuracy of reconstructing hierarchies.

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“…Therefore, many scholars have been attracted to studying social networks. Through the study of community structure, scholars have revealed the general characteristics of complex networks and promoted the in‐depth analysis of network relationships (da Fontoura Costa, 2022). And generally, users appear in multiple communities at the same time, that is, overlapping users appear, thus forming overlapping communities (Jiang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

An effective overlapping community merging method oriented to multidimensional attribute social networks

Cheng,

Yang,

Cheng

et al. 2023

Expert Systems

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For multidimensional attribute social networks, most of the existing methods have limitations in dealing with community redundancy, namely, the influences of the network's multidimensional attributes and users' interest preferences are ignored in the process of community merging. To this end, we propose an effective overlapping community merging method (EOCMM) oriented to multidimensional attribute social networks. In EOCMM, we focus on two core problems which are how to improve the method of improving overlapping community detection in multidimensional attribute social networks and how to merge the detected communities with high redundancy degree. To solve the first problem, based on the network topology characteristics and user interest preference, the Node Domain index is proposed to improve and optimize the selection of seed nodes. The traditional overlapping community detection method is improved by semantic fitness function and seed nodes. To solve the second problem, we merge the communities with high redundancy degree by Semantic Community Overlapping Degree which is fused by user similarity, community similarity and community tightness. Finally, we compared our method with other multiple mainstream methods by extensive experiments on three real social network datasets. The experimental comparison results show that the improvements on three evaluation metrics extension of modularity, partition density and semantic extension of modularity are 6.04%, 7.96% and 3.13%, respectively. And our method can make the results of community detection and merging more reasonable and effective.

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Coincidence complex networks

Cited by 16 publications

References 31 publications

Quantifying the hierarchical adherence of modular documents

Quantifying the hierarchical adherence of modular documents

Recovering hierarchies in terms of content similarity

An effective overlapping community merging method oriented to multidimensional attribute social networks

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