2020
DOI: 10.1109/tetc.2017.2751101
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Community Detection by Fuzzy Relations

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Cited by 35 publications
(15 citation statements)
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“…Moreover SOMs have been employed for a hierarchical clustering scheme for discovering latent gene expression patterns [25] and gene regulatory networks [26]. Given that the trained fuzzy cognitive maps can be represented as a fuzzy graph, clustering can be performed by fuzzy community discovery algorithms [27][28][29]. In Reference [30] fuzzy graphs have been used in a technique for estimating the number of clusters and their respective centroids.…”
Section: Previous Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Moreover SOMs have been employed for a hierarchical clustering scheme for discovering latent gene expression patterns [25] and gene regulatory networks [26]. Given that the trained fuzzy cognitive maps can be represented as a fuzzy graph, clustering can be performed by fuzzy community discovery algorithms [27][28][29]. In Reference [30] fuzzy graphs have been used in a technique for estimating the number of clusters and their respective centroids.…”
Section: Previous Workmentioning
confidence: 99%
“…Perhaps the most important figure of merit of this category is the topological error. The latter is defined in Equation (29). Figure 3 shows the average topological error as a percentage of the total number of data points after R 0 runs for each distance metric using the cosine rate.…”
Section: Topological Errormentioning
confidence: 99%
“…The CDFR and the CDFR-U algorithm based on the fuzzy relations were proposed by Luo et al [30], which the NGC node (Nearest node with Greater Centrality) plays an important role that determines the community label of each node. The centrality of each node is calculated according to the degree of each node and the degree of the direct neighbors, and then a special algorithm is used to find the NGC nodes and calculate the fuzzy relation between each node and its NGC node.…”
Section: Motivation a Related Workmentioning
confidence: 99%
“…To compare the performance of these algorithms, experiments are carried out by using the aforementioned generated networks, and the comparison of the experimental results given by each algorithm are shown in Table 1. Meanwhile, we also compare the NMI index given by the global community detection algorithms that include Louvain [15], SA [53], EO [54], LPA [27], EM [55], CDFR-U [30] (it is necessary to set a delta threshold to enlarge the gap between the central node of the community and the non-central node), and LIDGC. We can see that our algorithm guarantees good performance in most cases, especially in the first two networks, the performance of the algorithm reaches the highest value.…”
Section: A Random Networkmentioning
confidence: 99%
“…There are also some researchers apply DP algorithm to image processing [21], community detection [22]- [25], extracting multi-document abstracts [26] and noise removal [27].…”
Section: Algorithm 1 Dpmentioning
confidence: 99%