NeSiFC: Neighbors’ Similarity-Based Fuzzy Community Detection Using Modified Local Random Walk

Roy, Uttam K.; Muhuri, Pranab K.; Biswas, Sajib K.

doi:10.1109/tcyb.2021.3071542

Cited by 12 publications

(6 citation statements)

References 53 publications

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“…We use Python to implement the Algorithm 2 in this paper and select some classic network community detection algorithms and clear network community detection algorithms, such as GN [35] and D&L [39]. In addition, some advanced fuzzy network community detection algorithms are also used for comparisons, such as OCD [22] and NeSiFC [40].…”

Section: Experimental Results and Analysismentioning

confidence: 99%

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New Classifier Ensemble and Fuzzy Community Detection Methods Using POP Choquet-like Integrals

Zhang,

Jiang,

Wang

2023

Fractal Fract

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Among various data analysis methods, classifier ensemble (data classification) and community network detection (data clustering) have aroused the interest of many scholars. The maximum operator, as the fusion function, was always used to fuse the results of the base algorithms in the classifier ensemble and the membership degree of nodes to classes in the fuzzy community. It is vital to use generalized fusion functions in ensemble and community applications. Since the Pseudo overlap function and the Choquet-like integrals are two new fusion functions, they can be combined as a more generalized fusion function. Along this line, this paper presents new classifier ensemble and fuzzy community detection methods using a pseudo overlap pair (POP) Choquet-like integral (expressed as a fraction). First, the pseudo overlap function pair is proposed to replace the product operator of the Choquet integral. Then, the POP Choquet-like integrals are defined to perform the combinatorial step of ensembles of classifiers and to generalize the GN modularity for the fuzzy community network. Finally, two new algorithms are designed for experiments, and some computational experiments with other algorithms show the importance of POP Choquet-like integrals. All of the experimental results show that our algorithms are practical.

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Section: Experimental Results and Analysismentioning

confidence: 99%

“…It does not need to determine the partition number. In [40], the author used it to calculate the Karate Club network had a maximum modularity value of 0.372. As you can see, our network algorithm obtains a higher modularity value than other algorithms.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

New Classifier Ensemble and Fuzzy Community Detection Methods Using POP Choquet-like Integrals

Zhang,

Jiang,

Wang

2023

Fractal Fract

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“…4) Fuzzy agglomerative approach (e.g., FuzAg [17], Ne-SiFC [14]) usually merges small groups of nodes or even single node successively according to the similarity of neighbors. Such methods do not require the number of communities or other network prior information, but usually require effective similarity metrics and fuzzy membership calculation methods.…”

Section: Problem Formulation Of Fuzzy Community Detectionmentioning

confidence: 99%

“…Fuzzy community detection (FCD) can quantify the partial belongingness of a node to multiple communities, thus providing richer community structure information in a more fine-grained manner [13,14]. Specifically, FCD measures the belonging degree of each node to multi communities, named membership grade [15], which is valued in the continuous range of [0,1].…”

mentioning

confidence: 99%

Community detection from fuzzy and higher-order perspectives

Xiao,

2023

EPL

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Our ability to observe the mesoscale topology of complex networks through community detection has significantly advanced in the past decades. This progress has opened up new frontiers in discovering more sophisticated and meaningful community structures that possess fuzzy and higher-order characteristics. This review provides an overview of two emerging research directions, which are fuzzy and higher-order community detection. It includes related concepts and practical scenarios, mathematical descriptions and latest advancements, as well as current challenges and future directions. Therefore, it will facilitate researchers in swiftly grasping the two emerging fields, offering valuable insights for future development of community detection studies.

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“…The label propagation algorithm (LPA) [17][18] has approximately linear time complexity. On the other hand, outcomes observed from community detection are regularly in volatile.Roy U K proposed a modified local random walk method to catch the fuzzy community based on neighbors' similarity [19]. Xiaodong Li surveys recent theoretical advances in convex optimization approaches for community detection [20].…”

Section: Introductionmentioning

confidence: 99%

Community Detection Based on Fish School Effect

Sun

Chang

et al. 2022

IEEE Access

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Community structure has a more intuitive, physical meaning than how it has traditionally been perceived; this representation plays an important role in networks and is accordingly becoming widely adapted. Consequently, community detection has attracted increasing attention. Although scholars have proposed many community detection methods from different perspectives, due to the complexity, diversity and dynamic characteristics of networks, efficient community detection in many real networks remains a challenge. Inspired by fish school effect in real life, this paper envisions networks as an ecosystem and proposes a novel dynamic model that aims to reveal the communities in a more intuitive way. Relying on the new model, we design a community detection algorithm, known as community detection based on fish school effect (CDFSE). CDFSE has plentiful desirable properties: high-quality community detection, parameters free and notable scalability. To evaluate the performance of CDFSE, this paper employs two widely utilized evaluation metrics and eleven representative algorithms to test the effectiveness of the algorithm in both synthetic and real-world networks. The experimental results show that in most cases, CDFSE is superior to the comparison methods in terms of the quality of community detection.

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NeSiFC: Neighbors’ Similarity-Based Fuzzy Community Detection Using Modified Local Random Walk

Cited by 12 publications

References 53 publications

New Classifier Ensemble and Fuzzy Community Detection Methods Using POP Choquet-like Integrals

New Classifier Ensemble and Fuzzy Community Detection Methods Using POP Choquet-like Integrals

Community detection from fuzzy and higher-order perspectives

Community Detection Based on Fish School Effect

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