Community detection in complex networks using stacked autoencoders and crow search algorithm

Kumar, Sanjay; Mallik, Abhishek; Sengar, Sandeep Singh

doi:10.1007/s11227-022-04767-y

Cited by 16 publications

(4 citation statements)

References 48 publications

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“…To assess the performance of the proposed algorithm, we compare it with 7 recent methods introduced in studies: CD‐SACS, 28 DynCRep, 27 DynaMo, 21 D‐Louvain, 27 BBTA, 30 DPC‐DLP, 31 ECD 32 and IncNSA, 33 which are described in Section 2.…”

Section: Resultsmentioning

confidence: 99%

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Community detection by influential nodes based on random walk distance

Mokhtari,

Kherad

2024

Concurrency and Computation

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SummaryCommunity detection is a key feature that can be used to extract useful information from networks. In most studies, the same static algorithms are used on real‐time snapshots of the dynamic network. Such an action increases the calculations and the time taken for the clustering operation. The idea of community detection is based on the fact that communities are formed around a node that is more popular and influential. Therefore, in the proposed algorithm, first, several snapshots are received from the network, then for the first snapshot, the influence of each node is calculated using the influence function based on the random walk distance between nodes. Then by selecting k nodes with higher influence, network communities are formed and other nodes belong to the community with the most common edges. In the next step, the next snapshots will be received and then the communities will be updated. Then K nodes with higher influence are selected and their community is created if needed. The proposed method has been compared with state‐of‐the‐art algorithms, and the results show that proposed method has been able to have a suitable performance in the uniformity of communities and also the speed of community formation.

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Section: Resultsmentioning

confidence: 99%

“…In Reference 28 a novel community detection algorithm called CD‐SACS 4 proposed to uncover the community structure in complex networks. The algorithm utilizes stacked autoencoders, a type of deep learning model, for dimensionality reduction and feature selection.…”

Section: Related Workmentioning

confidence: 99%

Community detection by influential nodes based on random walk distance

Mokhtari,

Kherad

2024

Concurrency and Computation

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“…Denoising autoencoders usually add noise to the input to prevent the output from entering the input without learning features. A stacked Autoencoder (SAE) [98,99] is a variation of autoencoders that is built with stacks of multiple AEs to form a deep structure. First, an SAE is trained layer-by-layer in an unsupervised manner, then the pre-trained model is fine-tuned using the backpropagation and gradient descent method.…”

Section: B Ae and Related Modelsmentioning

confidence: 99%

Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey

Zhu,

Xiong,

Zhong

et al. 2024

IEEE Access

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Diabetic retinopathy (DR) is a microvascular disease that is associated with diabetes mellitus. DR can cause irreversible vision loss and blindness. DR classification, that is, early DR diagnosis and accurate DR grading, is critical for vision protection and immediate treatment. Deep learning-based automated systems led to significant expectations for DR classification based on fundus images with several advantages. In the past several years, many outstanding studies in this area have been conducted and several review articles have been published. However, the new trends and the future directions are need to further analyzed. Thus, we carefully included and read 94 related articles published from 2018 to 2023 through Web of Science, PubMed, Scopus, and IEEE Xplore. From this review, we found that transfer learning has been used as an outstanding strategy for overcoming the issue of the limited data resources to support DR analysis. CNN models of ResNet and VGGNet with layers of tens or even hundreds are the most popular frameworks used for DR classification. The APTOS 2019 and EyePACS are the most widely used datasets for DR classification. In addition, some lightweight DL architectures like SqueezeNet and MobileNet have been proposed for DR classification tasks, especially for limited data resources and computational capabilities. Although deep learning has achieved or surpassed human-level accuracy in DR classification, there is still a long way to go in real clinical workflows. Further improvements in model interpretability, trustworthiness from ophthalmologists, cost-effective and reliable DR screening systems are needed.

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“…The great success of graph representation learning [6] has recently provided a practical solution to this problem. Graph representation learning could aggregate features of nodes and network topology to improve the performance of tasks such as node classification and clustering [7][8][9][10][11]. The key point of graph representation is to better extract node feature information.…”

Section: Introductionmentioning

confidence: 99%

Hypergraph network embedding for community detection

Xiang,

You,

Wang

et al. 2024

J Supercomput

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Using attribute graphs for node embedding to detect community structure has become a popular research topic. However, most of the existing algorithms mainly focus on the network structure and node features, which ignore the higher-order relationships between nodes. In addition, only adopting the original graph structure will suffer from sparsity problems, and will also result in sub-optimal node clustering performance. In this paper, we propose a hypergraph network embedding (HGNE) for community detection to solve the above problems. Firstly, we construct potential connections based on the shared feature information of the nodes. By fusing the original topology with feature-based potential connections, both the explicit and implicit relationships are encoded into the node representations, thus alleviating the sparsity problem. Secondly, for integrating the higher-order relationship, we adopt hypergraph convolution to encode the higherorder correlations. To constrain the quality of the node embedding, the spectral hypergraph embedding loss is utilized. Furthermore, we design a dual-contrast mechanism, which draws similar nodes closer by comparing the representations of different views. This mechanism can efficiently prevent multi-node classes from distorting less-node classes. Finally, the dual-contrast mechanism is jointly optimized with self-training clustering to obtain more robust node representations, thus improving the clustering results. Extensive experiments on five datasets indicate the superiority and effectiveness of HGNE.

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Community detection in complex networks using stacked autoencoders and crow search algorithm

Cited by 16 publications

References 48 publications

Community detection by influential nodes based on random walk distance

Community detection by influential nodes based on random walk distance

Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey

Hypergraph network embedding for community detection

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