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.

People's influence on their friends' personal opinions and decisions is an essential feature of social networks. Due to this, many businesses use social media to convince a small number of users in order to increase awareness and ultimately maximize sales to the maximum number of users. This issue is typically expressed as the influence maximization problem. This paper will identify the most influential nodes in the social network during two phases. In the first phase, we offer a community detection approach based on the Node2Vec method to detect the potential communities. In the second phase, larger communities are chosen as candidate communities, and then the heuristicbased measurement approach is utilized to identify influential nodes within candidate communities. Evaluations of the proposed method on three real datasets demonstrate the superiority of this method over other compared methods.

For many years, fake news and messages have been spread in human societies, and today, with the spread of social networks among the people, the possibility of spreading false information has increased more than before. Therefore, detecting fake news and messages has become a prominent issue in the research community. It is also important to detect the users who generate this false information and publish it on the network. This paper detects users who publish incorrect information on the Twitter social network in Persian. In this regard, a system has been established based on combining context-user and context-network features with the help of a conditional generative adversarial network (CGAN) for balancing the data set. The system also detects users who publish fake news by modeling the twitter social network into a graph of user interactions and embedding a node to feature vector by Node2vec. Also, by conducting several tests, the proposed system has improved evaluation metrics up to 11%, 13%, 12%, and 12% in precision, recall, F-measure and accuracy respectively, compared to its competitors and has been able to create about 99% precision, in detecting users who publish fake news.

One of the most important security challenges with the advance of technology in cyberspace is phishing attacks. Phishing is a type of cyber-attack that always tries to obtain information such as username, password, bank account information, and the like by forging a website, email address and convincing the user to enter this information. Due to the increasing growth of these attacks and the increasing complexity of the type of attack, current phishing detection systems often cannot adapt to new attacks and have low detection accuracy. Graph-based methods are one of the techniques for identifying malicious domains that use the connections between the domain and IP to identify. In this paper, a graph-based phishing detection system using deep learning is presented. The main steps in the proposed method include extracting IP from the domain, defining the relationship between the domains, determining the weights, and converting the data to a vector by the Node2vec algorithm. Then, using CNN and DENSE deep learning models, the classification and identification operations are performed. The experimental results over three different datasets show that the proposed method provides an accuracy of about 99% in identifying malicious domains, which has an acceptable improvement compared to state of the art in this context.

Background: The link prediction issue is one of the most widely used problems in complex network analysis. Link prediction requires knowing the background of previous link connections and combining them with available information. The link prediction local approaches with node structure objectives are fast in case of speed but are not accurate enough. On the other hand, the global link prediction methods identify all path structures in a network and can determine the similarity degree between graph-extracted entities with high accuracy but are time-consuming instead. Most existing algorithms are only using one type of feature (global or local) to represent data, which not well described due to the large scale and heterogeneity of complex networks. Methods: In this paper, a new method presented for Link Prediction using node embedding due to the high dimensions of real-world networks. The proposed method extracts a smaller model of the input network by getting help from the deep neural network and combining global and local nodes in a way to preserve the network's information and features to the desired extent. First, the feature vector is being extracted by an encoder-decoder for each node, which is a suitable tool for modeling complex nonlinear phenomena. Secondly, both global and local information concurrently used to improve the loss function. More obvious, the clustering similarity threshold considered as the local criterion and the transitive node similarity measure used to exploit the global features. To the end, the accuracy of the link prediction algorithm increased by designing the optimization operation accurately. Results: The proposed method applied to 4 datasets named Cora, Wikipedia, Blog catalog, Drug-drug-interaction, and the results are compared with laplacian, Node2vec, and GAE methods. Experimental results show an average accuracy achievement of 0. 620, 0. 723, 0. 875, and 0. 845 on the mentioned datasets, and confirm that the link prediction can effectively improve the prediction performance using network embedding based on global similarity.