Community detection model for dynamic networks based on hidden Markov model and evolutionary algorithm

Abbood, Amenah D.; Attea, Bara'a Ali; Hasan, Ammar A.; Everson, Richard; Pizzuti, Clara

doi:10.1007/s10462-022-10383-2

Cited by 5 publications

(2 citation statements)

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“…It fused the three characteristics of temporal variability, stability, and continuity in dynamic social network communities. The HMM-MODCD 30 used a multi-objective evolutionary algorithm and Viterbi algorithm to formulate objective functions and provide temporal smoothness over time for detecting community in dynamic networks.…”

Section: /21mentioning

confidence: 99%

Identification of dynamic networks community by fusing deep learning and evolutionary clustering

Pan,

Yao,

Liu

et al. 2024

Preprint

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Community detection is a critical component of network analysis and a hot topic in social computing. Detecting community structure in dynamic networks has important theoretical and practical implications for understanding the intrinsic function of networks and predicting network behavior. However, the majority of existing dynamic community detection methods adopt shallow models and merely excavate the complex non-linear structure, which tends to generate undesirable community structure. In order to obtain the accurate and robust community structure in dynamic networks, we are inspired by network representation learning and utilize the deep learning to detect evolving communities in dynamic networks. In this paper, we propose a novel dynamic community detection method by fusing Deep Learning and Evolutionary Clustering(DLEC). This work attempts to combine deep learning and evolutionary clustering into a unified framework. First, we propose a matrix construction strategy to fully reveal the inherent community structures via the underlying community memberships. Then, we develop a novel multi-layer deep autoencoder framework that consists of multiple non-linear functions to extract the latent deep representation of the dynamic network. Based on the evolutionary clustering framework, a graph regularization term is introduced to ensure the smoothness of the community evolution. Finally, we employ the K-means clustering algorithm on the low-dimensional network space to obtain the community structure. Extensive experimental results on synthetic and real-world networks show that the proposed DLEC algorithm can effectively detect high-quality communities in the dynamic networks.

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Section: /21mentioning

confidence: 99%

Identification of dynamic networks community by fusing deep learning and evolutionary clustering

Pan,

Yao,

Liu

et al. 2024

Preprint

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“…In [10], novel secure video steganography dependent on a novel installing procedure had proposed. Video steganography had joined with video encoding.…”

Section: Related Workmentioning

confidence: 99%

Independent Video Steganalysis Framework Perspective of Secure Video Processing

Bilal A. Ozturk

2024

jes

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We have designed an algorithm of cross-domain feature set extraction for cross-domain video steganalysis of video steganography in multiple domains. For video steganography, we implemented the recently proposed steganography methods such as PMs, MVs, and IPMs. The outcomes of these techniques have fed as input to the proposed Steganalysis approach. We have designed a cross-domain technique for Steganalysis in which the global feature set is extracted according to common statistical properties. After the extraction of global features, the domain-specific features have been extracted in the local features set. Both global and local features are set to form the cross-domain steganalysis technique. The classification has been performed by using the conventional machine learning classifiers such as Support Vector Machine (SVM) and Artificial Neural Network (ANN).

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