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.

We define an algebraic theory of hierarchical graphs, whose axioms characterise graph isomorphism: two terms are equated exactly when they represent the same graph. Our algebra can be understood as a high-level language for describing graphs with a node-sharing, embedding structure, and it is then well suited for defining graphical representations of software models where nesting and linking ar...

Let T be a rooted and weighted tree, where the weight of any node is equal to the sum of the weights of its children. The popular Treemap algorithm visualizes such a tree as a hierarchical partition of a square into rectangles, where the area of the rectangle corresponding to any node in T is equal to the weight of that node. The aspect ratio of the rectangles in such a rectangular partition ne...

In this paper, a new steganography scheme with high embedding payload and good visual quality is presented. Before embedding process, secret information is encoded as block using Reed-Muller error correction code. After data encoding and embedding into the low-order bits of host image, modulus function is used to increase visual quality of stego image. Since the proposed method is able to embed...

Many social networks have a bipartite nature. Link prediction in has been the focus of interest for many researchers recently. Network embedding, which maps each node network to low-dimensional feature vector is used solve problems. The aim this study investigate how embedding enhance link performance networks. A and supervised learning based model presented input learned vectors pairs obtained...

Attributed network embedding (ANE) is to learn low-dimensional vectors so that not only the structure but also node attributes can be preserved in space. Existing ANE models do consider specific combination between graph and attributes. While each has its structural characteristics, such as highly-interconnected neighbors along with their certain patterns of attribute distribution, node's neigh...

Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn representations a lower-dimension space while preserving the structures and semantics for downstream tasks (e.g., node/graph classification, node clustering, link prediction), has drawn considerable attentions recent years. In t...

Identifying and quantifying structural dissimilarities between complex networks is a fundamental challenging problem in network science. Previous comparison methods are based on the features, such as length of shortest path, degree graphlet, which may only contain part topological information. Therefore, we propose an efficient method embedding, i.e., \textit{DeepWalk}, considers global In deta...

Information network mining often requires examination of linkage relationships between nodes for analysis. Recently, network representation has emerged to represent each node in a vector format, embedding network structure, so off-the-shelf machine learning methods can be directly applied for analysis. To date, existing methods only focus on one aspect of node information and cannot leverage no...

Deep learning has been successful in various domains including image recognition, speech recognition and natural language processing. However, the research on its application in graph mining is still in an early stage. Here we present Model R, a neural network model created to provide a deep learning approach to the link weight prediction problem. This model uses a node embedding technique that...