A novel smart vaccination method is proposed in this paper to distribute a limited number of vaccines among the people of a large community, such as a country, consisting of smaller communities like cities or provinces. The proposed method is comprised of two phases; A vaccine allocation phase and a targeted vaccination phase. In the first phase, the available vaccines are allocated to the communities based on demoGraphics and the effectiveness of each type of vaccine. In the second phase, each community is modelled as a contact Graph, and the vaccines available to the community are administered to the individuals whose vaccination has the greatest impact on breaking the chain of transmission. As a result of utilizing the Node2Vec Graph Embedding algorithm, the complexity of the proposed method increases linearly with the number of people in the community, as opposed to common centrality based methods, the complexities of which increase with the square or cube of the number of individuals. Furthermore, the proposed method can distribute multiple types of vaccines with different probabilities of effectiveness. The performance of the proposed method is comparable to the common centrality based vaccination methods, while its complexity is lower. The results of the simulation show a 20% decrease in the peak number of infected individuals.

The economic downturn in recent years has had a significant negative impact on corporates performance. In the last two years, as in the last years of 2010s, many companies have been influenced by the economic conditions and some have gone bankrupt. This has led to an increase in companies' financial risk. One of the significant branches of financial risk is the emph{company's credit risk}. Lenders and investors attach great importance to determining a company's credit risk when granting a credit facility. Credit risk means the possibility of default on repayment of facilities received by a company. There are various models for assessing credit risk using statistical models or machine learning. In this paper, we will investigate the machine learning task of the binary classification of firms into bankrupt and healthy based on the emph{spectral Graph theory}. We first construct an emph{adjacency Graph} from a list of firms with their corresponding emph{feature vectors}. Next, we first embed this Graph into a one-dimensional Euclidean space and then into a two dimensional Euclidean space to obtain two lower-dimensional representations of the original data points. Finally, we apply the emph{support vector machine} and the emph{multi-layer perceptron} neural network techniques to proceed binary emph{node classification}. The results of the proposed method on the given dataset (selected firms of Tehran stock exchange market) show a comparative advantage over PCA method of emph{dimension reduction}. Finally, we conclude the paper with some discussions on further research directions.

Special conditions of wireless sensor networks, such as energy limitation, make it essential to accelerate the convergence of algorithms in this field, especially in the distributed compressive sensing (DCS) scenarios, which have a complex reconstruction phase. This paper presents a DCS reconstruction algorithm that provides a higher convergence rate. The proposed algorithm is a distributed primal-dual algorithm in a bidirectional incremental cooperation mode where the parameters change with time. The parameters are changed systematically in the convex optimization problems in which the constraint and cooperation functions are strongly convex. The proposed method is supported by simulations, which show the higher performance of the proposed algorithm in terms of convergence rate, even in stricter conditions such as the small number of measurements or the lower degree of sparsity.

Link prediction in knowledge Graphs addresses predicting missing entities or relations of a knowledge Graph, typically using knowledge Graph Embedding techniques. Training these models on low-resource-language knowledge Graphs presents unique challenges, which have not been thoroughly addressed in the literature, and correspondingly there is no benchmark for evaluating link prediction methods on such Graphs. These knowledge Graphs often have unique topologies due to the characteristics of low-resource languages. Many knowledge Graphs are derived from encyclopedias like Wikipedia, which in low-resource languages may have many propositions from common subjects and/or facts and few from less common ones, leading to distinctive topologies in the extracted knowledge Graph. FarsBase, as the knowledge Graph related to the Persian language, exemplifies these properties. Originating from Persian Wikipedia, it has some relation-types with many numbers of instances and some other relation types with very few instances. This paper introduces "FarsPishBin," a lightly-pruned version of FarsBase, as a benchmark for low-resource-language knowledge Graph Embedding. The authors argue that translational models are likely to outperform other Embedding models on this benchmark. To check the mentioned hypothesis, the popular Embedding models are evaluated on FarsPishBin and the experiments prove that translational models (as expected) perform best. This benchmark aims to serve as a standard platform for future-coming models addressing link prediction in low-resource-language knowledge Graphs.

Knowledge Graphs are widely used tools in the field of reasoning, where reasoning is facilitated through link prediction within the knowledge Graph. However, traditional methods have limitations, such as high complexity or an inability to effectively capture the structural features of the Graph. The main challenge lies in simultaneously handling both the structural and similarity features of the Graph. In this study, we employ a constraint satisfaction approach, where each proposed link must satisfy both structural and similarity constraints. For this purpose, each constraint is considered from a specific perspective, referred to as a view. Each view computes a probability score using a GRU-RNN, which satisfies its own predefined constraint. In the first constraint, the proposed node must have a probability of over 0.5 with frontier nodes. The second constraint computes the Bayesian Graph, and the proposed node must have a link in the Bayesian Graph. The last constraint requires that a proposed node must fall within an acceptable fault. This allows for N-N relationships to be accurately determined, while also addressing the limitations of Embedding. The results of the experiments showed that the proposed method improved performance on two standard datasets.

For a given measure space (X, B, m) we construct all measure spaces (Y, C, l) in which (X, B, m) is embeddable. The construction is modeled on the ultrafilter construction of the Stone-Cech compactification of a completely regular topological space.Under certain conditions the construction simplifies. Examples are given when this simplification occurs.

In this paper we consider to embed a tree T with N vertices on a set of N points inside a simple polygon on n vertices and the goal is to minimize the number of bends. The main idea of our algorithm is modeling the problem into Graph matching problem and uses the Graph matching algorithms. We apply the concept of error-correction transformation and find the appropriate cost function then we perform the Graph matching with the minimum cost for minimizing the number of bends. The time complexity of the proposed algorithm is found to be O (N2n+N4).

Nowadays, whereas the use of social networks and computer networks is increasing, the amount of associated complex data with Graph structure and their applications, such as classification, clustering, link prediction, and recommender systems, has risen significantly. Because of security problems and societal concerns, anomaly detection is becoming a vital problem in most fields. Applications that use a heterogeneous Graph, are confronted with many issues, such as different kinds of neighbors, different feature types, and differences in type and number of links. So, in this research, we employ the HetGNN model with some changes in loss functions and parameters for heterogeneous Graph Embedding to capture the whole Graph features (structure and content) for anomaly detection, then pass it to a VAE to discover anomalous nodes based on reconstruction error. Our experiments on AMiner data set with many base-lines illustrate that our model outperforms state-of-the-arts methods in heterogeneous Graphs while considering all types of attributes.