[Extendable Functions with a Dense Graph]

Gibson

doi:10.2307/44153671

Multi-scale Mixed Dense Graph Convolution Network for Skeleton-based Action Recognition

IEEE Access ◽

10.1109/access.2020.3049029 ◽

2021 ◽

pp. 1-1

Author(s):

Hailun Xia ◽

Xinkai Gao

Keyword(s):

Action Recognition ◽

Dense Graph ◽

Multi Scale

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Semi-Supervised Locality Preserving Dense Graph Neural Network With ARMA Filters and Context-Aware Learning for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2021.3100578 ◽

2021 ◽

pp. 1-12

Author(s):

Yao Ding ◽

Xiaofeng Zhao ◽

Zhili Zhang ◽

Wei Cai ◽

Nengjun Yang ◽

...

Keyword(s):

Neural Network ◽

Image Classification ◽

Hyperspectral Image ◽

Context Aware ◽

Hyperspectral Image Classification ◽

Dense Graph ◽

Locality Preserving

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Approximate Nearest Neighbor Search Using Query-Directed Dense Graph

Database Systems for Advanced Applications. DASFAA 2021 International Workshops - Lecture Notes in Computer Science ◽

10.1007/978-3-030-73216-5_29 ◽

2021 ◽

pp. 429-444

Author(s):

Hongya Wang ◽

Zeng Zhao ◽

Kaixiang Yang ◽

Hui Song ◽

Yingyuan Xiao

Keyword(s):

Nearest Neighbor ◽

Nearest Neighbor Search ◽

Approximate Nearest Neighbor Search ◽

Dense Graph ◽

Approximate Nearest Neighbor ◽

Neighbor Search

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Ramsey properties of randomly perturbed graphs: cliques and cycles

Combinatorics Probability Computing ◽

10.1017/s0963548320000231 ◽

2020 ◽

Vol 29 (6) ◽

pp. 830-867 ◽

Cited By ~ 1

Author(s):

Shagnik Das ◽

Andrew Treglown

Keyword(s):

Random Graph ◽

High Probability ◽

Graph Model ◽

Significant Progress ◽

Random Choice ◽

Ramsey Problem ◽

Random Graph Model ◽

Dense Graph ◽

Precise Solution ◽

Analogous Question

AbstractGiven graphs H1, H2, a graph G is (H1, H2) -Ramsey if, for every colouring of the edges of G with red and blue, there is a red copy of H1 or a blue copy of H2. In this paper we investigate Ramsey questions in the setting of randomly perturbed graphs. This is a random graph model introduced by Bohman, Frieze and Martin [8] in which one starts with a dense graph and then adds a given number of random edges to it. The study of Ramsey properties of randomly perturbed graphs was initiated by Krivelevich, Sudakov and Tetali [30] in 2006; they determined how many random edges must be added to a dense graph to ensure the resulting graph is with high probability (K3, Kt) -Ramsey (for t ≽ 3). They also raised the question of generalizing this result to pairs of graphs other than (K3, Kt). We make significant progress on this question, giving a precise solution in the case when H1 = Ks and H2 = Kt where s, t ≽ 5. Although we again show that one requires polynomially fewer edges than in the purely random graph, our result shows that the problem in this case is quite different to the (K3, Kt) -Ramsey question. Moreover, we give bounds for the corresponding (K4, Kt) -Ramsey question; together with a construction of Powierski [37] this resolves the (K4, K4) -Ramsey problem.We also give a precise solution to the analogous question in the case when both H1 = Cs and H2 = Ct are cycles. Additionally we consider the corresponding multicolour problem. Our final result gives another generalization of the Krivelevich, Sudakov and Tetali [30] result. Specifically, we determine how many random edges must be added to a dense graph to ensure the resulting graph is with high probability (Cs, Kt) -Ramsey (for odd s ≽ 5 and t ≽ 4).To prove our results we combine a mixture of approaches, employing the container method, the regularity method as well as dependent random choice, and apply robust extensions of recent asymmetric random Ramsey results.

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