A multi-objective adaptive evolutionary algorithm to extract communities in networks

Li, Qi; Cao, Zehong; Ding, Weiping; Li, Qing

doi:10.1016/j.swevo.2019.100629

Cited by 39 publications

(16 citation statements)

References 34 publications

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“…In addition to verifying the effectiveness of the DDRL-NRA algorithm, we verify the flexibility of the algorithm by changing the resource requirements of end user function requests. Specifically, we adjust the storage resource requirements of the initial end user function requests to [1,20], [1,10], and then conduct experiments from three aspects: resource allocation revenue, end user request acceptance rate and revenue rate. Finally, we also show the comparison of the joint optimization results under three different storage resource requirements.…”

Section: Results and Analysis 1) Training Performance Verificationmentioning

confidence: 99%

“…Dynamic and intelligent physical network resource management is one of the core businesses of SAGIN [15], [16]. Physical network resource management methods based on heuristic or offline algorithms, such as particle swarm algorithm [17], simulated annealing algorithm [18], genetic algorithm, etc., have all played an active role in physical network resource allocation [19], [20]. It cannot be ignored that the above algorithms have many limitations in practical applications.…”

Section: Introductionmentioning

confidence: 99%

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Incorporating Distributed DRL into Storage Resource Optimization of Space-Air-Ground Integrated Wireless Communication Network

Wang

Liu

Jiang

et al. 2022

Preprint

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Section: Results and Analysis 1) Training Performance Verificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Incorporating Distributed DRL into Storage Resource Optimization of Space-Air-Ground Integrated Wireless Communication Network

Wang

Liu

Jiang

et al. 2022

Preprint

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“…From the experimental and simulation studies on real life networks, the proposed model was found more effective than the state-of-the-art approaches, including those proposed by [20] and [41]. Recently, several studies were published based the work of Amelio and Pizzuti, such as those of Sani et al [47], and Li et al [48]. Attea et al [49] focused on CD problem reformulation as a MOO model for simultaneous detection of intra-and inter-community structures; a heuristic perturbation operator was also suggested for emphasizing the detection of the intra-and inter-community connections in order to establish a positive relation with the MOO model, which is given as follows:…”

Section: A Community Detection: Historical Overviewmentioning

confidence: 93%

The Influence of NMI against Modularity in Community Detection Problem: A Case Study for Unsigned and Signed Networks

Abdulrahman

Abbood

Attea

2021

eijs

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Community detection is useful for better understanding the structure of complex networks. It aids in the extraction of the required information from such networks and has a vital role in different fields that range from healthcare to regional geography, economics, human interactions, and mobility. The method for detecting the structure of communities involves the partitioning of complex networks into groups of nodes, with extensive connections within community and sparse connections with other communities. In the literature, two main measures, namely the Modularity (Q) and Normalized Mutual Information (NMI) have been used for evaluating the validation and quality of the detected community structures. Although many optimization algorithms have been implemented to unfold the structures of communities, the influence of NMI on the Q, and vice versa, between a detected partition and the correct partition in signed and unsigned networks is unclear. For this reason, in this paper, we investigate the correlation between Q and NMI in signed and unsigned networks. The results show that there is no direct relationship between Q and NMI in both types of networks.

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“…For the guided matting results in Fig. 2, the images are enhanced firstly [32][33], then they are segmented by using the improved graph-based, the region merging [27,34] based mean-shift model [35] and edge detection [36][37] algorithms respectively. The experimental results are shown in Fig.…”

Section: Fine-segmentation On Graph-based Algorithm and Comparing Wit...mentioning

confidence: 99%

Improved Minimum Spanning Tree based Image Segmentation with Guided Matting

2022

KSII TIIS

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In image segmentation, for the condition that objects (targets) and background in an image are intertwined or their common boundaries are vague as well as their textures are similar, and the targets in images are greatly variable, the deep learning might be difficult to use. Hence, a new method based on graph theory and guided feathering is proposed. First, it uses a guided feathering algorithm to initially separate the objects from background roughly, then, the image is separated into two different images: foreground image and background image, subsequently, the two images are segmented accurately by using the improved graph-based algorithm respectively, and finally, the two segmented images are merged together as the final segmentation result. For the graph-based new algorithm, it is improved based on MST in three main aspects: (1) the differences between the functions of intra-regional and inter-regional; (2) the function of edge weight; and (3) re-merge mechanism after segmentation in graph mapping. Compared to the traditional algorithms such as region merging, ordinary MST and thresholding, the studied algorithm has the better segmentation accuracy and effect, therefore it has the significant superiority.

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A multi-objective adaptive evolutionary algorithm to extract communities in networks

Cited by 39 publications

References 34 publications

Incorporating Distributed DRL into Storage Resource Optimization of Space-Air-Ground Integrated Wireless Communication Network

Incorporating Distributed DRL into Storage Resource Optimization of Space-Air-Ground Integrated Wireless Communication Network

The Influence of NMI against Modularity in Community Detection Problem: A Case Study for Unsigned and Signed Networks

Improved Minimum Spanning Tree based Image Segmentation with Guided Matting

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