مجله رایانش نرم و فناوری اطلاعات
سال دوازدهم شماره 3 (پاییز 1402)

Social networks are one of the types of complex networks. Identifying communities in social networks is an effective way to use their information, for which several algorithms have been presented so far. In this paper, novel algorithms are designed, in which a learning automaton is attached to each node; The number of actions of learning automata is fixed and equal to the estimate of the number of network communities. At each step, each of the learning automata chooses an action from its set of actions. Choosing any of these actions means assigning the label of that community to the node. The action chosen by each automaton is evaluated based on the chosen actions of its neighbors ((local attention) and/or communities detected by the entire method (global screening). The result of the evaluation leads to generate rewards or punish signal for the automata. By receiving a reward, the probability of re-choosing the chosen action by the automaton, or the community label, increases, and otherwise, by receiving a fine, the probability of this action decreases. By repeating the algorithm, the optimal action is determined as long as no change occurs in the selected label of the corresponding automata of each node with more iterations, and as a result, the optimal communities are determined as the output of the algorithm. The comparison of the results of the experiments shows the effectiveness of the proposed methods in comparison with the previous methods.

The crisis of trust between users is one of the most important challenges of social networks. One solution to this challenge is for users to know about the trust level of other users. This solution can be implemented by quantifying trust and using collective intelligence. Various models have been presented to calculate trust in social networks using mathematical formulas and without considering the permanent changes in users' trust based on various events. However, providing an approach that can model various types of scenarios and assumptions and calculate and evaluate the trust level of each user is a very important issue. In this paper, the Colored Petri net, a graphical formalism, has been used to model trust and distrust between users in social networks and to evaluate the impact of various events on the change of the trust level of each user over time. Furthermore, an algorithm is proposed in this paper to determine the influence of each user on the trust level of another user, so that this influence is heterogeneous between users. The proposed approach can model any number of users, define different scenarios by simply changing the model parameters' values, and evaluate each user's trust level in each scenario. The proposed modeling approach which the results obtained from its models are validated with the OMNeT++ simulator and statistical analysis can be used as a simulator of users' trust level in social networks.

One of the most important issues in social networks is the high volume of rumors that are spread by human or machine ‎agents. In such situations, automatic detection of rumors to keep public opinion safe from their potential dangers is of ‎great importance. In this research, using deep learning techniques, a new solution for automatically detecting rumors ‎related to epidemic diseases in social networks will be presented. In the proposed method, first the content of existing ‎messages is prepared for processing in the next steps. Also, weight matrix format has been used to describe content ‎characteristics. Then, in the second step of the proposed method, the convolutional neural network is used to extract the ‎set of suitable features from the matrix of features obtained from the previous step. In this way, the matrix of content ‎features is used as the input of the deep neural network, and the weight values obtained in the last fully connected layer of ‎this neural network are used as the features extracted from it. Finally, the aggregation of several binary classifiers is used ‎in order to detect rumors and classify the features extracted through convolutional neural network. For this purpose, the ‎extracted features are simultaneously processed by several learning models and the final output of the proposed system is ‎determined by voting the outputs of these three algorithms. The results of this research show that by using the proposed ‎method, rumors can be detected with an average accuracy of 98.8%, which shows an improvement of at least 2.4% in ‎detection accuracy compared to the previous methods.‎

Question Answering Systems (QAS) are one of the most important intelligent systems that have the ability to provide instant and explicit answers to input questions. One of the new challenges of these systems is the ability to answer multi-hop complex questions that require information collection from multiple documents. In this paper, a method is proposed to solve the challenge of answering multi-hop complex questions. In this method, first, documents related to the question are retrieved in a two-step process. Then, to facilitate answering the question, knowledge is extracted from the retrieved documents and represented in the form of a graph. Finally, to find the answer to the question, reasoning is performed on the graph using a combination of heterogeneous graph neural network and transformer. To evaluate the effectiveness of the proposed method, this method and other related works have been tested on the HotpotQA open-domain dataset. The results obtained in answering questions based on F1 score and exact match are reported as 86.51 and 78.71 respectively, indicating superiority of this method over similar works.

Discovering Communities is a fundamental problem in understanding network performance for social network analysis. Traditional community detection methods merely consider the network topologies. Nonetheless, social media contains valuable data about people's interests, concerns, and sentiments, which is not reflected in the structure. The semantic solutions eventuate to the mislay of precious structural information. Most existing combinatorial methods favor one of the mentioned kinds and have limited performance. This paper introduces a 2-phase way based on fuzzy inferences to determine effective social network communities.  A series of real-life and synthetic networks have been used to evaluate the proposed method compared with several relevant algorithms. The experimental results proved that the proposed approach performs better in detecting meaningful communities and is more effective concerning network coherence and node attributes.

The statistical-based software fault localization approaches highly depend on the program inputs and become unstable as input data changes. Therefore, generating appropriate test data plays an essential role in the quality of the software fault-localization process. This paper presents an approach to improving program fault localization by generating new test data. The minimized test suite determines the faulty execution path and the fault suspiciousness of each statement in the path is generated. First, the suspicious statements in the faulty path are determined. To this aim, the conditions of the faulty execution path are contradicted from the end to the beginning, and test data is created for the desired path using the Z3 solver. Afterward, the program under test is executed with the generated test data using the Concolic testing technique. The fault-suspicious branch is determined depending on the passing or failing of the program execution. As a result, the region of statements for applying the causal-statistical approach is minimized. The proposed approach is evaluated on the four projects in the Defects4J benchmark. The results show that 75% of faults are localized by examining a maximum of 1% of the program's source code. Compared to the related work, the results have improved by 17.98%. Moreover, the mean number of sentences examined for fault localization decreases by 16.78% in the worst case.

Sybil attacks are increasingly growing and expanding in social networks. A malicious user with a fake identity, known as a Sybil attack, can create a large number of fake accounts to generate spam, impersonate other users, commit fraud, and gain access to many legitimate users' information. For security reasons, such fake accounts should be identified and disabled. Various identification methods have been proposed to deal with fake accounts. However, most of these methods detect fake accounts using social structure graphs, which leads to poor performance, or use machine learning methods, which have low accuracy for identifying Sybil attacks. In this paper, a hybrid clustering method called CRNM is proposed. The proposed method is based on clustering, so that by combining different community detection methods; A new community detection method is presented. The combination of these methods has led to higher accuracy, more reliable results and more stability. The CRNM method has been evaluated on datasets collected from Twitter, Reddit, Instagram and Facebook. Unlike other machine learning-based approaches, the proposed method focuses on different levels of user profile features. The evaluation results have shown that the CRNM method detects Sybil nodes with an accuracy of 85.13%.