Efficient Algorithms for Densest Subgraph Discovery

Fang, Yixiang; Yu, Kaiqiang; Cheng, Reynold; Lakshmanan, Laks V. S.; Lin, Xuemin

doi:10.48550/arxiv.1906.00341

Cited by 3 publications

(2 citation statements)

References 64 publications

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“…Deep generative networks for image inpainting. The conventional image inpainting methods [2,3,5,7,8,18,27] focus on finding useful patches for recovering the damaged image regions. However, the semantic information of image regions is out of consideration in these methods, thus yielding unsatisfactory results in complex scenes.…”

Section: Related Workmentioning

confidence: 99%

MISF: Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting

Li¹,

Guo²,

Dong³

et al. 2022

Preprint

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Section: Related Workmentioning

confidence: 99%

MISF: Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting

Li¹,

Guo²,

Dong³

et al. 2022

Preprint

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“…The available image inpainting approaches can be divided into traditional and deep learning approaches. Traditional approaches [13][14][15][16][17][18][19] aattempt to find patches from the background region to restore the hole. These methods only produce better results on images with simple cases, but the effectiveness becomes worse when handling images with complex texture and large missing areas.…”

Section: Image Inpaintingmentioning

confidence: 99%

A Novel Image Inpainting Method Used for Veneer Defects Based on Region Normalization

Chen

Lou

et al. 2022

Sensors

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The quality of the veneer directly affects the quality and grade of a blockboard made of veneer. To improve the quality and utilization of a defective veneer, a novel deep generative model-based method is proposed, which can generate higher-quality inpainting results. A two-phase network is proposed to stabilize the network training process. Then, region normalization is introduced to solve the inconsistency problem between the mean and standard deviation, improve the convergence speed of the model, and prevent the model gradient from exploding. Finally, a hybrid dilated convolution module is proposed to reconstruct the missing areas of the panels, which alleviates the gridding problem by changing the dilation rate. Experiments on our dataset prove the effectiveness of the improved approach in image inpainting tasks. The results show that the PSNR of the improved method reaches 33.11 and the SSIM reaches 0.93, which are superior to other methods.

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Semantic community query in a large‐scale attributed graph based on an attribute cohesiveness optimization strategy

Ge,

Sun,

Lin

et al. 2024

Expert Systems

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The task of a semantic community query is to obtain a subgraph based on a given query vertex (or vertex set) and other query parameters in an attributed graph such that belongs to , contains and satisfies a predefined community cohesiveness model. In most cases, existing community query models based on the network structure for traditional attributed networks usually lack community semantics. However, the features of vertex attributes, especially the attributes of the query vertices, which are closely related to the community semantics, are rarely considered in an attributed graph. Existing community query algorithms based on both structure cohesiveness and attribute cohesiveness usually do not take the attributes of the query vertex as an important factor of the community cohesiveness model, which leads to weak semantics of the communities. This paper proposes a semantic community query method named in a large‐scale attributed graph. First, the k‐core structure model is adopted as the structure cohesiveness of our community query model to obtain a subgraph of the original graph. Second, we define attribute cohesiveness based on the average distance between the query vertices and other vertices in terms of attributes in the community to prune the subgraph and obtain the semantic community. In order to improve the community query efficiency in large‐scale attributed graphs, applies two heuristic pruning strategies. The experimental results show that our method outperforms the existing community query methods in multiple evaluation metrics and is ideal for querying semantic communities in large‐scale attributed graphs.

show abstract

Efficient Algorithms for Densest Subgraph Discovery

Cited by 3 publications

References 64 publications

MISF: Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting

MISF: Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting

A Novel Image Inpainting Method Used for Veneer Defects Based on Region Normalization

Semantic community query in a large‐scale attributed graph based on an attribute cohesiveness optimization strategy

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