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Amid global economic integration and changes in international patterns, strategic emerging industries have become the core driving force for high-quality economic development and industrial structure upgrading. Studying the structural evolution of the global trade network of strategic environmental products and its influence mechanism can help reveal the changes in the industrial division of labour and guide the formulation of green trade policies. Based on this, this paper analyses the evolution of trade networks of products in the fields including energy-saving technologies, new materials, renewable energy, etc., using social network analysis and an exponential random graph model with 50 countries around the world as the research object from 2010 to 2022. It is found that the global trade network of strategic environmental products has been affected by the decoupling of the international situation, and trade links have been reduced, forming three major independent groups, with a stable core-edge structure, and the United States, Germany, and China playing an important pivotal role. Factors such as economic fundamentals, R&D expenditures, and trade openness have a positive impact on network formation, while external factors and internal structural features of the network also influence network evolution.

The microservice architecture breaks through the traditional cluster architecture mode based on virtual machines and uses containers as carriers to interact through lightweight communication mechanisms to reduce system coupling and provide more flexible system service support. With the expansion of the system scale, a large number of system logs with complex structures and chaotic relationships are generated. How to accurately analyze the system logs and make efficient fault prediction is particularly important for building a safe and reliable system. By studying neural network technology, this paper proposes an Attention-Based Bidirectional Long Short-Term Memory Network (Bi-LSTM). Combined with the dual channel convolutional neural network model (DCNN), it uses the attention mechanism to explore the differences between dimensional features, realizes multi-dimensional feature fusion, and establishes a BiLSTM-DCNN deep learning model that integrates the attention mechanism. From the perspective of social network analysis, a data preprocessing method is proposed to process fault redundant data and improve the accuracy of fault prediction under Microservices. Compare BiLSTM-DCNN with the mainstream system log analysis machine learning models SVM, CNN and Bi-LSTM, and explore the advantages of BiLSTM-DCNN in processing microservice system log text. The model is applied to simulation data and HDFS data set for experimental comparison, which proves the good generalization ability and universality of BiLSTM-DCNN.

This paper undertakes a social network analysis of two science fiction television series, Stargate and Star Trek. Television series convey stories in the form of character interaction, which can be represented as “character networks”. We connect each pair of characters that exchanged spoken dialogue in any given scene demarcated in the television series transcripts. These networks are then used to characterize the overall structure and topology of each series. We find that the character networks of both series have similar structure and topology to that found in previous work on mythological and fictional networks. The character networks exhibit the small-world effects but found no significant support for power-law. Since the progression of an episode depends to a large extent on the interaction between each of its characters, the underlying network structure tells us something about the complexity of that episode’s storyline. We assessed the complexity using techniques from spectral graph theory. We found that the episode networks are structured either as (1) closed networks, (2) those containing bottlenecks that connect otherwise disconnected clusters or (3) a mixture of both.

Link prediction has been widely applied in social network analysis. Existing studies on link prediction assume the network to be undirected, while most realistic social networks are directed. In this paper, we design a simple but effective method of link prediction in directed social networks based on common interest and local community. The proposed method quantifies the contributions of neighbors with analysis on the information exchange process among nodes. It captures both the essential motivation of link formation and the effect of local community in social networks. We validate the effectiveness of our method with comparative experiments on nine realistic networks. Empirical studies show that the proposed method is able to achieve better prediction performance under three standard evaluation metrics, with great robustness on the size of training set.

This paper presents two semi-definite programming (SDP) based methods to solve the Key Player Problem (KPP). The KPP is to identify a set of $k$ nodes (i.e., key players) from a social network of size $n$ such that the number of nodes connected to these $k$ nodes is maximized. The KPP has applications in social diffusion and products adoption as it helps maximizing information diffusion and impact. We first formulate the KPP as an integer program (IP) and then convert it into an SDP formulation, which can be solved efficiently and produce a set of high quality candidate solutions. We develop an IP-based algorithm and a stochastic search (greedy) algorithm to find the final solution for the KPP. We compare our algorithms with existing methods in small and large networks with different network structures, including random graph, scale-free network, and community-based scale-free network (CSN). Computational results show that our algorithms are more efficient in solving the KPP in all networks. In addition, we examine how the network structure influences the nodes coverage. It is found that CSNs allow the highest nodes coverage due to their community and scale-free structure.

The need for enterprises to manage project portfolio risks over the life cycle has become increasingly prominent. It is essential to evaluate and manage them to achieve project portfolios and organizations’ success. Unlike project risk, project portfolio risk is more complex and uncertain due to risk interactions. Risk management is unsatisfactory in project portfolios due to the lack of awareness of risk interactions and the life cycle. The purpose of this paper is to identify the critical risks of project portfolios over the life cycle considering risk interactions. We primarily verified 20 identified risks through a questionnaire survey and an expert interview method and evaluated the interactions among them using the Delphi method. Furthermore, risk interactions were analyzed using the social network analysis (SNA) methodology to determine the important risks. Finally, a comprehensive evaluation of important risks was carried out to identify critical risks according to the evaluation principles. The results identified six critical portfolio risks, two key risk contagion paths and revealed risk characteristics of different life cycle phases. This research considerably contributes to the body of knowledge pertaining to project portfolio management that will enable organizations that implement project portfolios and similar multi projects to emphasize critical risks.

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

In complex network analysis, the local community detection problem is getting more and more attention. Because of the difficulty to get complete information of the network, such as the World Wide Web, the local community detection has been proposed by researcher. That is, we can detect a community from a certain source vertex with limited knowledge of an entire graph. The previous methods of local community detection now are more or less inadequate in some places. In this paper, we have proposed a new local modularity metric $G$ and based on it, a two-phase algorithm is proposed. The method we have taken is a greedy addition algorithm which means adding vertices into the community until $G$ does not increase. Compared with the previous methods, when our method is calculating the modularity metric, the range of vertices what we considered may affect the quality of the community detection wider. The results of experiments show that whether in computer-generated random graph or in the real networks, our method can effectively solve the problem of the local community detection.

In todays world, identity of human beings has expanded beyond the real world to the cyber world. Virtual identity of millions of users is present at various web-based Social Networking Sites (SNSs) such as Myspace, Facebook, and Twitter. Interactions through SNSs have become a part of our daily practices, which eventually leaves a big trail of behavioral pattern in virtual domain. In this paper, the authors examined the feasibility of person identification using such social network activities as behavioral biometrics. Experimentation includes extraction of a number of idiosyncratic features from SNSs and analysis of their performance as novel social behavioral biometric features.

The problem of discovering influential users is important to understand and analyze online social networks. The user profiles and interactions between users are significant features to evaluate the user influence. As these features are heterogeneous, it is challengeable to take all of them into a proper model for influence evaluation. In this paper, we propose a model based on personal user features and the adjacent factor to discover influential users in online social networks. Through taking the advantages of Bayesian network and chain principle of PageRank algorithm, the features of the user profiles and interactions are integratedly considered in our model. Based on real data from Sina Weibo data and multiple evaluation metrics of retweet count, tweet count, follower count, etc., the experimental results show that influential users identified by our model are more powerful than the ones identified by single indicator methods and PageRank-based methods.

Topological link prediction is the task of assessing the likelihood of new future links based on topological properties of entities in a network at a given time. In this paper, we introduce a multistrain bacterial diffusion model for link prediction, where the ranking of candidate links is based on the mutual transfer of bacteria strains via physical social contact. The model incorporates parameters like efficiency of the receiver surface, reproduction rate and number of social contacts. The basic idea is that entities continuously infect their neighborhood with their own bacteria strains, and such infections are iteratively propagated on the social network over time. The probability of transmission can be evaluated in terms of strains, reproduction, previous transfer, surface transfer efficiency, number of direct social contacts i.e. neighbors, multiple paths between entities. The value of the mutual strains of infection between a pair of entities is used to rank the potential arcs joining the entity nodes. The proposed multistrain diffusion model and mutual-strain infection ranking technique have been implemented and tested on widely accepted social network data sets. Experiments show that the MSDM-LP and mutual-strain diffusion ranking technique outperforms state-of-the-art algorithms for neighbor-based ranking.

We analyze a topological structure of networks formed according to the entries and trackbacks in the blogosphere, which is a collection of weblog articles. The analysis is performed based on community extraction, network visualization and keyword analysis. It is shown that the large-scale structure of the blogosphere has a globally sparse, but locally dense structure. The entries in a community yield a dense structure while the trackbacks that interconnect communities are sparse. The visualized results show sparkling-firework-like patterns. We then attempt to characterize the communities using a tf-idf technique. It is found that specific topics are discussed in each community. These results will help us to identify the communities in which certain specific topics discussed and to detect trends in the blogosphere.

Complex systems — when treated as systems accessible to natural sciences — pose tremendous requirements on data. Usually these requirements obstruct a scientific understanding of social phenomena on scientific grounds. Due to developments in IT, new collective human behavior, new dimensions of data sources are beginning to open up. Here we report on a complete data set of an entire society, consisting of over 350,000 human players of a massive multiplayer online game. All actions of all players over three years are recorded, including communication behavior and social ties. In this work we review the first steps undertaken in analyzing this vast data set, focusing on social dynamics on friend-, enemy- and communication networks. This new data-driven approach to social science allows to study socio-economic behavior of humans and human groups in specific environments with unprecedented precision. We propose two new empirical social laws which relate the network properties of link weight, overlap and betweenness centrality in a nonlinear way, and provide strong quantitative evidence for classical social balance assumptions, the weak ties hypothesis and triadic closure. In our analysis of large-scale multirelational networks we discover systematic deviations between positive and negative tie networks. Exploring such virtual "social laboratories" in the light of complexity science has the potential to lead to the discovery of systemic properties of human societies, with unforeseen impact on managing human-induced crises.

To what extent does joint membership in intergovernmental organizations (IGOs) matter for bilateral trade? How and under what conditions do the various types of IGOs — economic, socio-cultural and general purpose — influence bilateral trade between their members? How do complex interdependencies in world trade matter? Existing research tends to examine aggregate joint IGO memberships and has done little to analyze how specific types of IGO membership matter in trade. Using a detailed IGO dataset and a novel network analysis approach called the temporal exponential random graph model, I assess the importance of three main IGO types — economic, socio-cultural and general purpose — in helping members to establish major trading ties. The results provide support for general purpose and socio-cultural IGOs and point to the importance of network phenomena such as popularity, activity and transitivity effects. Moreover, joint economic IGO memberships exhibit slightly more complex relations with bilateral trade. A robustness test reveals that preferential trade agreements are significant in fostering trade, while the World Trade Organization and other economic IGOs such as development banks are not. This paper presents a nuanced way of analyzing IGOs and provides the impetus for the study of complex interdependencies in international trade.

In the last two years, we have seen a huge number of debates and discussions on COVID-19 in social media. Many authors have analyzed these debates on Facebook and Twitter, while very few ones have considered Reddit. In this paper, we focus on this social network and propose three approaches to extract information from posts on COVID-19 published in it. The first performs a semi-automatic and dynamic classification of Reddit posts. The second automatically constructs virtual subreddits, each characterized by homogeneous themes. The third automatically identifies virtual communities of users with homogeneous themes. The three approaches represent an advance over the past literature. In fact, the latter lacks studies regarding classification algorithms capable of outlining the differences among the thousands of posts on COVID-19 in Reddit. Analogously, it lacks approaches able to build virtual subreddits with homogeneous topics or virtual communities of users with common interests.

Nowadays, many online users find the selection of information and required products challenging due to the growing volume of data on the web. Recommender systems are introduced to deal with information overload. Cold start and data sparsity are the two primary issues in these systems, which lead to a decrease in the efficiency of recommender systems. To solve the problems, this paper proposes a novel method based on social network analysis. Our method leverages a multi-agent system for clustering users and items and predicting relationships between them simultaneously. The information on users and items is extracted from the user-item matrix as distinct graphs. Each of the graphs is then treated as a social network, which is further processed and analyzed by community detection and link prediction procedures. The users are grouped into several clusters by the community detection agent, which results in each cluster as a community. Then link prediction agent identifies the latent relationships between users and items. Simulation results show that the proposed method has significantly improved performance metrics as compared to recent techniques.

Managing knowledge of plant maintenance in a power utility company is vital to the provision of a safe and reliable electricity supply to two million domestic and commercial customers. The business is built on a lot of expertise, techniques and experience of all of its employees from engineering, maintenance, safety and environmental control, to quality assurance. Knowledge auditing is usually carried out as the first critical step in the implementation of any Knowledge Management programmes in power utility companies. Although various knowledge auditing approaches have been proposed by some researchers and practitioners, there is a lack of a systematic approach in the way it is conducted, and the audit practice varies with different industries and companies. This paper presents a systematic knowledge audit approach, which has been successfully trial-implemented in a power plant.

Studying the school readiness is an interesting domain that has attracted the attention of the public and private sectors in education. Researchers have developed some techniques for assessing the readiness of preschool kids to start school. Here we benefit from an integrated approach which combines Data Mining (DM) and social network analysis towards a robust solution. The main objective of this study is to explore the socio-demographic variables (age, gender, parents' education, parents' work status, and class and neighbourhood peers influence), achievement data (Arithmetic Readiness, Cognitive Development, Language Development, Phonological Awareness), and data that may impact school readiness. To achieve this, we propose to apply DM techniques to predict school readiness. Real data on 306 preschool children was used from four different elementary schools: (1) Life school for Creativity and Excellence a private school located in Ramah village, (2) Sisters of Saint Joseph missionary school located in Nazareth, (3) Franciscan missionary school located in Nazareth and (4) Al-Razi public school located in Nazareth, and white-box classification methods, such as induction rules were employed. Experiments attempt to improve their accuracy for predicting which children might fail or dropout by first, using all the available attributes; next, selecting the best attributes; and finally, rebalancing data and using cost sensitive classification. The outcomes have been compared and the models with the best results are shown.

Due to easy and cost-effective ways, communication has amplified many folds among humans across the globe irrespective of time and geographic location. This has led to the construction of an enormous and a wide variety of social networks that is a network of social interactions or personal relations. Social network analysis (SNA) is the inspection of social networks in order to understand the participant’s arrangement and behaviour. Discovering communities from the social network has become one of the key research areas in SNA. Communities discovered from social networks facilitate its members so as to interact with relatable people who have similar or comparable interests. However, in present time, the enormous growth of social networks demands an intensive investigation of recent work carried out for identifying community division in social networks. This paper is an attempt to enlighten the ongoing developments in the domain of Community detection (CD) for SNA. Additionally, it sheds light on the algorithms which use meta-heuristic optimisation techniques to hit upon the community structure in social networks. Further, this paper gives a comparison of proposed methods in recent years and most frequently used optimisation approaches in the domain of CD. It also describes some application areas where CD methods have been used. This guides and encourages researchers to probe and take ahead the work in the area of detecting communities from social networks.

The purpose of this research was to further the understanding of knowledge exchange within organisations by examining how the dyadic relationships between individuals, in terms of the channels of communication used (structural capital), knowledge awareness (cognitive capital), and the quality of their relationships (relational capital), influence opportunities for knowledge exchange (access to advice), and ultimately individual performance. data were analysed using social network analysis to determine individual network centralities, and structural equation modelling was used to test the hypotheses at the individual level. The findings suggest (1) face-to-face channels with trusted sources are the most preferred method for exchanging sensitive knowledge, (2) knowing where expertise resides and source availability is key to research knowledge exchange, and (3) centrality in knowledge network does not result in uniform increases in individual performance. While technology has the potential to increase the efficiency of knowledge exchange by removing the barriers to same-time, same-place interactions, computer-mediated communication may actually inhibit the exchange of tacit knowledge and advice because of the lean medium of the exchange, negatively impacting performance. Using a network perspective, this study adds to the literature on intra-organisational learning networks by examining how an individual’s use of different communication channels to share knowledge is related to centrality in knowledge networks, and how this impacts individual performance.