In this paper, we investigate the use of manifold learning techniques to enhance the separation properties of standard graph kernels. The idea stems from the observation that when we perform multidimensional scaling on the distance matrices extracted from the kernels, the resulting data tends to be clustered along a curve that wraps around the embedding space, a behaviour that suggests that lon...

Graph embedding is an e‚ective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data, but they have mostly ignored the data distribution of the latent codes from the graphs, which o‰en results in inferior embedding in real-worl...

Embedding large graphs in low dimensional spaces has recently attracted significant interest due to its wide applications such as graph visualization, link prediction and node classification. Existing methods focus on computing the embedding for static graphs. However, many graphs in practical applications are dynamic and evolve constantly over time. Naively applying existing embedding algorith...

In this paper, a faster method for embedding cryptographic information in the image ispresented by expressing the concept of latent prints (Steganography). Data is encrypted bytwo methods before embedding to increase reliability. Then they are embedded into the imageby a button, a method has been expressed to reduce potential noise impact on the wayinformation is encoded.

the notion of strong arcs in a fuzzy graph was introduced bybhutani and rosenfeld in [1] and fuzzy end nodes in the subsequent paper[2] using the concept of strong arcs. in mordeson and yao [7], the notion of“degrees” for concepts fuzzified from graph theory were defined and studied.in this note, we discuss degrees for fuzzy end nodes and study further someproperties of fuzzy end nodes and fuzz...

Currently, deep learning-based methods are widely used in the fault diagnosis of time-series data for their high precision. However, application traditional learning models is limited by calculational efficiency and poor interpretation ability. To address problems, a model named parallel relation network (DPTRN) proposed this article. There three main advantages DPTRN. First, our time relations...

Embedding static graphs in low-dimensional vector spaces plays a key role network analytics and inference, supporting applications like node classification, link prediction, graph visualization. However, many real-world networks present dynamic behavior, including topological evolution, feature diffusion. Therefore, several methods for embedding have been proposed to learn representations over ...

In the natural and social systems of real world, various network can be seen everywhere. The world where people live as a combination with different dimensions. Link prediction formalizes interaction behavior between people. Traditional link methods mainly study user static network. This article studied dynamic graph representation learning so to put forward an improved model in Besides, intera...

lymph node dissection is of prime importance for accurate staging of colorectal carcinomas. since a great number of small lymph nodes are missed in the traditional method, several fat clearing solutions have been introduced for easier detection of smaller lymph nodes. in this study we evaluated the efficacy of a new fat clearing solution so-called lymph node revealing solution (lnrs) in colecto...

background: the sentinel lymph node (sln) is defined as the first chain node in the lymphatic basin that receives primary lymphatic flow. if the sln is negative for metastatic disease, then other nodes are expected to be disease-free. sln techniques have been extensively applied in the staging and treatment of many tumors, including melanoma, breast and vulvar cancers. this study aims to evalua...