social network

In recent years, social networks have become an integral part of people's lives and play a significant role in the real world. The primary aim of influence maximization problem is finding a set of nodes in the network which can maximize the influence if the diffusion process starts from them. Therefore, the problem’s goal is to find influential people in large scale real social networks. The penetration phenomenon is carried out according to an influence model in the network. Two independent cascade and linear threshold influence models, the most common of which is the independent cascade model, are utilized for broadcasting in the network. Theoreticaly, optimizing the selection influential nodes problem is NP-hard in both models. The problem will start by considering the social network’s graph, a specific influence model and a given number k. The problem’s goal is to select k nodes (users) from the graph (network) as influential nodes, so that the number of active nodes is maximized at the end of the diffusion process. Due to the influence maximization problem and finding influential people is an NP-hard optimization problem in the social network, meta-heuristic algorithms can be used to solve the problem. With regard to the privous researches, there is just one objective function as the problem’s goal and it is maximizing the number of effective nodes of the diffusion model. While other objective functions such as maximizing the number of effective nodes in the diffusion model and minimizing the budget value k (the number of initial nodes as seed) are not considered, minimizing the time required for effective diffusion can be achieved by having k initial nodes. Although various models for the problem and various optimization algorithms have been presented to discover influential nodes in social networks, paying attention to the multi-objective nature of the problem and improving the performance of the proposed optimization algorithms are a serious research challenge in this field. In this paper. A fuzzy version of the NSGA-II as a multi-objective genetic algorithm, whose mutation and crossover rates are adjusted by Fuzzy Inference System, is utilized to simultaneously optimize the three objectives of maximizing the number of effective nodes, minimizing the number of initial nodes and minimizing the required diffusion time. In the proposed method, the Expected Diffusion Value (EDV) of the diffusion model is replaced instead the simulation of the independent cascade diffusion model with heavy calculations to calculate the diffusion spread (the number of effective nodes of the diffusion model). Therefore, the EDV function is satisfied as the thied objective (minimizing the required diffusion time). The second objective function can also be converted into a maximization function by considering N-k nodes, where k is the number of selected initial nodesand N is the total graph nodes. The decimal numerical coding with fixed length is used in the proposed method. Based on the coding, each chromosome has k genes in the search space. The integer part of each gene is the selected node number. The decimal part is also used to determine whether that initial node exists or not. In the maximizing influence process using the fuzzy NSGA-II algorithm, the solution space (chromosomes) consists of k number of initial nodes, which should be encoded into the fuzzy NSGA-II comprehensible space. Also, a Fuzzy Inference System is proposed to adjust the mutation and recombination rates for filling up a serious challenge of genetic algorithms. In the fuzzy system, NF and FitBest are considered as two input variables, and Pm (mutation rate) and Pc (crossover rate) are returned as outputs NF is the number of chromosomes in the first frontier (F1) of the multi-objective genetic algorithm and FitBest is the average of the normalized objective functions for the chromosomes in F1. To analysis the efficiency of the proposed method, the obtained exprimental results have been compared with conventional maximizing influence methods, i.e., degree centrality, distance centrality, closeness centerality, betweenness, eigenvector and page rank methods, non-fuzzy version of NSGA-II, the latest methods presented for maximizing penetration based on multi-objective meta-heuristic algorithms, i.e. µGP multi-objective evolutionary algorithm, multi-objective crow search algorithm (MOCSA), greedy randomized adaptive search process algorithm (GRASP) and the Multi-Transformation Evolutionary Framework (MTEF) on five benchmark graph datasets Arenasjazz, Canetscience, EgoFacebook, Higgs-Reply and Slashdot. This comparison is based on four criteria: EDV, cost (the number of nodes selected as seed), the influence expansion criterion σ(s), i.e. the number of active nodes with the independent cascade (IC) propagation model, and the execution time of the method in seconds. The obtained results show the superiority of the proposed method over the other methods.

The Social Internet of Things (SIoT) network is the result of the union of the Social Network and the Internet of Things network; wherein, each object tries to use the services provided by its friends. In this network, to find the right friend in order to use the right service is demanding. Great number of objects' friends, in classical algorithms, causes increasing the computational time and load of network navigation to find the right service with the help of friendly objects. In this article, in order to reduce the computational load and network navigation, it is proposed, firstly, to select the appropriate object friend from a heuristic approach; secondly, to use an adapted binary cuckoo optimization algorithm (AB-COA) which tries to select the appropriate friendly object to receive the service according to the maximum response capacity of each friendly object, and finally, adopting the Adamic-Adar local index (AA) with the interest degree centrality criterion so that it represents the characteristics of the common neighbors of the objects are involved in the friend selection. Finally, by executing the proposed algorithm on the Web-Stanford dataset, an average of 4.8 steps was obtained for reaching a service in the network, indicating the superiority of this algorithm over other algorithms.

Nowadays, high-entropy alloys (HEAs) are a popular domain for researchers which is improved performance by using machine learning (ML). HEAs are formed at least five main elements with close or equal size which is depend on their size and type of elements to extend physical and mechanical features. The ML approach has many applications in various fields. Social network analysis (SNA) is one of the ML tools that is used graph theory. Each graph consists of a number of nodes and edges that each node has its own descriptors. The studies done so far has not used the high-entropy alloys network dataset based on the similarity of content and structural features of each compound. In this paper, a new method is proposed that generalized SNA tools to metallurgical and materials engineering. The proposed method is investigated the HEAs descriptors, in which HEAs descriptors similarity are calculated and the HEAs interaction network is created. The groups have been extracted by Louvain algorithm which each group called cluster. The clusters have alloys with similar properties. The experimental results shown high quality clusters that will be effective in predicting the compounds functionality and discovering new compounds and descriptors. The modularity criterion indicates the quality of the clusters, is about 0.713.

Nowadays, social networks play an important role in people's daily lives. A social network is a kind of social structure that consists of several nodes that can be individuals or organizations. Most importantly, these nodes are connected by one or more specific types of dependencies, such as friendships or work relationships. Understanding the structure and constituent groups of these networks can give us useful information about the state of society and individuals. In this article, a new solution to solve the problem of social structure detection is provided. Social structure means communities or associations in social networks. An important issue of this context is network graph construction based on objects as nodes and edges as transactions between these objects. Community detection is based on these graphs. The appropriate solution is to identify and create clusters of nodes that have strong connections with each other and at the same time have weaker connections between nodes of different clusters. Optimization algorithms can be used to construct and detect these connections. Harmonic search is one of the efficient optimization algorithms in this context. However, in the field of identifying communication structures and communities, so far, no research work has been done using the harmonic search algorithm. In this paper, a new method is proposed to construct network clusters based on network graphs. It can identify effective communications based on different criteria. In order to propose this method, first a new version of harmonic search algorithm is designed for discrete environments while the original version of which is for continuous environments. Then, according the problem, which is to discover appropriate structures in the social network graph, a new method is devised to solve it. This method tries to provide a suitable discrete version by relying on different operators to be applicable to solve the desired problem. In order to evaluate the proposed method, various experiments were carried out on several different networks. These networks have been used as benchmark in previous research work. For evaluation and comparison, two artificial networks and two real networks are considered. The evaluation results of the proposed method on these networks are presented based on different criteria and compared with four previous algorithms that are known in this field. Comparison results show that the proposed algorithm is relatively superior to other algorithms or at least produces similar results. The most important reason that can justify the relative performance superiority of the proposed algorithm or at least its competitive results is the better search capability of the problem search space. This leads to the discovery of more promising points and the production of better solutions.

The proposed algorithm in this research is based on the multi-agent particle swarm optimization as a collective intelligence due to the connection between several simple components which enables them to regulate their behavior and relationships with the rest of the group according to certain rules. As a result, self-organizing in collective activities can be seen. Community structure is crucial for many network systems, the algorithm uses a special type of coding to identify the number of communities without any prior knowledge. In this method, the modularity function is used as a fitness function to optimize particle swarm. Several experiments show that the proposed algorithm which is called Multi Agent Particle Swarm is superior compared with other algorithms. This algorithm is capable of detecting nodes in overlapping communities with high accuracy.The point in using the previously presented PSO algorithms for community detection is that they recognize non-overlapping communities, and this goes back to the representation of genes by these methods, but the use of multi-agent collective intelligence by our algorithm has led to the identification of nodes in overlapping communities.The results show that the nodes that are shared between a set of agents, these nodes are active nodes that create an overlap in the communities. Our experimental results show that when a member node is more than one community, this node is a good candidate to be selected as the active node, which has led to the creation of overlapping networks.

Recently, using social networks increases and people propagate their information through this networks. One of the most important challenges in these networks is sentiment attack, in which the attacker spreads rumors to influence users. Therefor rumor detection become important and attracts expanding research attention. Most of the previous works using deep neural networks for rumor detection without special preprocessing but we propose a new method for preprocessing data before learning which improve results. we use LSTM-CNN architecture with cyclical learning rate to detect Persian rumors. Beside that we investigate BERT model for Persian tweets. Our results demonstrate the effectiveness of this approach for English and Persian rumor detection.

The trust inference problem in a social network is defined as anticipating the trust level that a user can have to another user who is not directly connected to him on the trust network. This research aims to propose a method for trust inference in a trust network. Previous research studies are mainly limited to one type of trust network, and they cannot be used for different trust networks with different values of trust. In this research, soft computing and neural network model are used to predict trust values. To train the neural network system, genetic algorithm is exploited. One of the main advantages of the proposed method is that, unlike previous methods, it is not limited to one type of trust network, and it can also be used for trust networks with different values of trust. In the proposed method, at first four proposed features are extracted from the trust network, and afterward, the proposed neural network system is trained using these features as well as the genetic algorithm. The proposed method is implemented on the standard trust network and is compared with other similar methods. Experimental results indicate that the proposed method is able to produce more accurate results in comparison with previous methods.

Entities in social networks, in addition to having the relationship with each other, also have content. This type of networks can be modeled by the enriched graph, in which nodes could have text too. Graph clustering is one of the important attempts toward analyzing social networks. Despite these two facts, most of the existing graph clustering methods independently focused on one of the content or structural aspects. Content-Structural graph clustering algorithms simultaneously consider both the structure and the content of the graph. The main aim of this paper is to achieve well connected (structured) clusters while their nodes benefit from homogeneous attribute values (content). The proposed algorithm in this paper so-called RSL-Cluster performs the clustering by hierarchically removing the edge between nodes which has a weight lower that the average similarity of nodes. This stage continues until reaching the user’s desired number of clusters. Comparing the proposed algorithm with three well-known content-structural clustering algorithms represents the proper functioning of the proposed method. The used measures to evaluate our method include structural, content and the content-structural measures.