Evolution Of Social Networks Research Articles

Social network structure modulates neural activities underlying group norm processing: evidence from event-related potentials

IntroductionSocial ties play a crucial role in determining the health and wellbeing of individuals. However, it remains unclear whether the capacity to process social information distinguishes well-connected individuals from their less-connected peers. This study explored how an individual’s social network structure influences the dynamic processing of group norms, utilizing event-related potentials (ERPs).MethodsThe study involved 43 university students from the same class who participated in a social network study measuring metrics such as real-life social network size, in-degree, out-degree, and betweenness centrality. Subsequently, 27 students participated in an EEG study assessing their willingness to engage in various exercises after being exposed to peer feedback or in its absence.ResultsThe results indicate that an individual’s social network structure is significantly associated with the dynamic processing of group norms. Notably, well-connected individuals exhibited larger ERP amplitudes linked to feedback (e.g., N200, P300, and LPP), greater functional segregation within the brain network (e.g., local efficiency and clustering coefficient), and enhanced synchronization within frontal area and across different brain areas.DiscussionThese findings highlight that well-connected individuals possess enhanced sensitivity and efficiency in processing social information, pointing to potential areas for further research on the factors influencing social network evolution.

Com_Tracker: a two-phases framework for detecting and tracking community evolution in dynamic social networks

PurposeThis paper aims to introduce a novel modularity-based framework, Com_Tracker, designed to detect and track community structures in dynamic social networks without recomputing them from scratch at each snapshot. Despite extensive research in this area, existing approaches either require repetitive computations or fail to capture key community behavioral events, both of which limit the ability to generate timely and actionable insights. Efficiently tracking community structures is crucial for real-time decision-making in rapidly evolving networks, while capturing behavioral events is necessary for understanding deeper community dynamics. This study addresses these limitations by proposing a more efficient and adaptive solution. It aims to answer the following questions: How can we efficiently track community structures without recomputation? How can we detect significant community events over time?Design/methodology/approachCom_Tracker models dynamic social networks as a sequence of snapshots. First, it detects the community structure of the initial snapshot using a static community detection algorithm. Then, for each subsequent time step, Com_Tracker updates the community structure based on the previous snapshot, allowing it to track communities and detect their changes over time. The locus-based adjacency encoding scheme is adopted, and Pearson’s correlation guides the construction of neighboring solutions.FindingsExperiments conducted on various networks demonstrate that Com_Tracker effectively detects community structures and tracks their evolution in dynamic social networks. The results highlight its potential for real-time tracking and provide promising performance outcomes.Practical implicationsCom_Tracker offers valuable insights into community evolution, helping practitioners across fields such as resource management, public security, marketing and public health. By understanding how communities evolve, decision-makers can better allocate resources, enhance targeted strategies and predict future community behaviors, improving overall responsiveness to changes in network dynamics.Originality/valueCom_Tracker addresses critical gaps in existing research by combining the strengths of modularity maximization with efficient tracking of community changes. Unlike previous methods that either recompute structures or fail to capture behavioral events, Com_Tracker provides an incremental, adaptive framework capable of detecting both community evolution and behavioral changes, enhancing real-world applicability in dynamic environments.

Rewiring the organizational network: Corporate offsites and network tie formation

AbstractResearch SummarySocial networks are integral to collaborative work, but research on network change has shed little light on the mechanisms firms use to stimulate collaborative network ties among their employees. In this study, we examine the effects of corporate offsites on the evolution of social networks within an organization. We find that offsites lead to rewiring of intraorganizational networks, but with a surprising asymmetry: they stimulate everyone to initiate more collaboration ties, but only those who attend the offsite receive more ties. These results are consistent with a conceptualization of offsites as direct interventions that focus on social interactions for those who attend, but also as indirect interventions that signal the value of collaboration to everyone, even those who do not attend.Managerial SummaryCorporate offsites are events that convene people from across a firm to interact outside their regular work environment. Despite their popularity, this article offers the first data‐driven analysis of their effectiveness in promoting collaboration among employees. Offsites facilitate employees' awareness of who knows what, build trust, and foster interpersonal affect among employees. This study shows that offsites have a dual effect on the social network of an organization: they prompt everyone in the firm to initiate more collaborative ties following an offsite, but those who actually attend the offsite attract more of those collaborative tie requests. Therefore, offsites can be a useful tool to boost collaboration—with benefits accruing both to the individuals whose networks grow and to the firm in which they work.

Research on the dynamic spread of information in social networks based on relationship strength theory and feedback mechanism

Introduction: Studying the main factors and related paths of rumor propagation contributes to the precise governance of rumor information in social networks. Most existing network representation learning methods do not fit with real-world information propagation networks well, and the network cannot effectively model the temporal characteristics and dynamic evolution features of rumor information propagation.Methods: Our study proposes a new dynamic network representation model for information propagation. Additionally, the study introduces a feedback mechanism where the latest node representations are fed back to neighboring nodes.Results: The method solves the problem of delayed network representation and improves network representation performance.Discussion: We conducted experimental simulations, and the results indicate that a higher level of trust contributes to stable group relationships and converging opinions, reducing the likelihood of opinion dispersion. Furthermore, novelty of topics, and interactivity between users, and opinion leaders exhibit distinct characteristics in guiding public opinion. The viewpoint evolution of the newly constructed dynamic network representation model aligns more closely with viewpoint evolution in real-world social networks.

Social Network Forensics Analysis Model Based on Network Representation Learning.

The rapid evolution of computer technology and social networks has led to massive data generation through interpersonal communications, necessitating improved methods for information mining and relational analysis in areas such as criminal activity. This paper introduces a Social Network Forensic Analysis model that employs network representation learning to identify and analyze key figures within criminal networks, including leadership structures. The model incorporates traditional web forensics and community algorithms, utilizing concepts such as centrality and similarity measures and integrating the Deepwalk, Line, and Node2vec algorithms to map criminal networks into vector spaces. This maintains node features and structural information that are crucial for the relational analysis. The model refines node relationships through modified random walk sampling, using BFS and DFS, and employs a Continuous Bag-of-Words with Hierarchical Softmax for node vectorization, optimizing the value distribution via the Huffman tree. Hierarchical clustering and distance measures (cosine and Euclidean) were used to identify the key nodes and establish a hierarchy of influence. The findings demonstrate the effectiveness of the model in accurately vectorizing nodes, enhancing inter-node relationship precision, and optimizing clustering, thereby advancing the tools for combating complex criminal networks.

Understanding the Rivalry Among Telecommunication Companies in the Cross-Border Patent Infringement: An Evolutionary Social Network Analysis

Understanding the rivalry in infringement-litigation dynamics is critical for firms in the telecommunication industry. While the number of cross-border patent disputes in these years has surged owing to the globalized economy, the existing literature primarily focuses on patent disputes among telecommunication companies in the national jurisdiction. Hence, to capture the rivalry in the telecommunication industry more comprehensively, we utilize the evolutionary social network to explore the patent dispute data in the International Trade Commission (ITC), which is a crucial legal battleground for enterprises with international business. Through developing a dynamic litigation network and discussing the possible role of the positions in compliance with their interacting relationship, we illustrated that companies involved in ITC investigations can be segmented into key players, frequent respondents, strategic applicants, as well as peripheral enterprises, and the litigation strategies implicated behind four groups of firms stress the strategic nature of ITC investigations. According to the findings, this study contributes to the literature on patent litigation in the telecommunication field and social network relationships. In addition, this paper suggests that telecommunication companies should develop litigation strategies based on their positions and roles in the competition network, and policymakers need to have a deeper understanding of the competition practices in the global legal context.

Group Behavior Prediction and Evolution in Social Networks

Group Behavior Prediction and Evolution in Social Networks

A New Perspective for Computational Social Systems: Fuzzy Modeling and Reasoning for Social Computing in CPSS

The evolution of modern mobile terminals, social networks, and other intelligent services makes everyone become a ubiquitous information perceiver, producer, and propagator. Also known as “social sensor” and “social IoT”, these individuals and communities generate a huge volume of social signals, which has shown prominent value for mining. These unstructured social signals provide a new perspective in the research of complex systems, which makes the traditional cyber–physical system (CPS)-oriented information computing sublimate to the cyber–physical–social system (CPSS)-oriented knowledge computing. However, there still exist great uncertainties, ambiguities, and complexities in modeling behaviors of social individuals or groups. Especially when we apply big-data-driven learning-based models in specific fields and scenarios, the lack of domain expert knowledge and characteristics of system uncertainty severely limits the performance and accuracy of these models. The introduction of fuzzy system modeling integrates data and knowledge in the social computing area, which has shown its unique advantages in solving the above issues and has drawn more attention to this topic. In this article, we conduct a review of recent advances in social computing with fuzzy technologies in CPSS. First, we briefly review the development of social computing, and analyze the characteristics and advantages of social computing through fuzzy methods. Second, we refine core fuzzy system methods for social computing and elaborate on existing fuzzy-technology-empowered social computing methodologies. As in a range of social spaces, we also review and analyze related advances in human-in-the-loop systems. We also reveal the trend of decentralized, autonomous, and organized computing in cyber–physical–social space with fuzzy-based methods and proposed a framework to categorize related studies in CPSS. Finally, we conclude the research trends and hotspots based on current studies, and discuss the challenges for future research directions.

Social Media and Nutritional Habits among nutrition students: A Social Work Perspective

Introduction: According to the research conducted in recent years and after the evolution of social networks, the statistics of obesity and the consumption of fast foods and high-calorie foods have increased significantly. Also, food blogging has become much more widespread and popular in the space of social networks, so we are investigating the impact of social networks on people's nutritional habits.Material and Methods: The purpose of this research is to examine the relationship and impact of social networks on people's nutritional habits. For this research, a 20-question questionnaire was answered by 60 college students and the obtained data was analyzed by SPSS 26.Results: The most social media has been used are Instagram, Telegram, and YouTube, and 22% use social media between 3 and 4 hours a day. More than 58% used ready food less than 2 days a week. But 56% of them reported that social media didn’t have a noticeable effect on their informed decision in choosing food.Conclusion: This study showed that the use of social media can be effective in people's lifestyle and food pattern and food intake.

Simulating emergence of novelties using agent-based models

Understanding the growth and evolution of social networks is an important area of study, as these networks form the foundation for many popular online services such as social networking sites (SNS) and online games. However, previous models developed to explain the growth mechanisms of these networks have struggled to accurately reproduce certain behaviors that are frequently observed in real data, such as waves of novelty, in which new individuals or topics receive more attention than existing ones for a short period of time. In this study, we introduce a new model that incorporates context information into existing agent-based models in order to more accurately capture the structure and growth dynamics of these networks. Context information is introduced through labels based on the timing of appearance and relationships with antecedent agents. New agents are first added to the network when they are called by existing agents, and at this time they are also given a label. Agents added to the network at the same time by the same agent will have the same label. These labels are used to classify agents and give them different selection probabilities. This newly introduced selection probability creates a mechanism in which new agents receive attention beyond preferential attachment. By comparing the results of our model with real data on ten metrics, we demonstrate that it is able to produce behavior that more closely resembles real data. This improved understanding of the dynamics of social networks has important implications for designing effective interventions, including strategies for user acquisition and retention.

A Dynamic Emotional Propagation Model over Time for Competitive Environments

Emotional propagation research aims to discover and show the laws of opinion evolution in social networks. The short-term observation of the emotional propagation process for a predetermined time window ignores situations in which users with different emotions compete over a long diffusion time. To that end, we propose a dynamic emotional propagation model based on an independent cascade. The proposed model is inspired by the interpretable factors of the reinforced Poisson process, portraying the “rich-get-richer” phenomenon within a social network. Specifically, we introduce a time-decay mechanism to illustrate the change in influence over time. Meanwhile, we propose an emotion-exciting mechanism allowing prior users to affect the emotions of subsequent users. Finally, we conduct experiments on an artificial network and two real-world datasets—Wiki, with 7194 nodes, and Bitcoin-OTC, with 5881 nodes—to verify the effectiveness of our proposed model. The proposed method improved the F1-score by 3.5% and decreased the MAPE by 0.059 on the Wiki dataset. And the F1-score improved by 0.4% and the MAPE decreased by 0.013 on the Bitcoin-OTC dataset. In addition, the experimental results indicate a phenomenon of emotions in social networks tending to converge under the influence of opinion leaders after a long enough time.

A multilayer network-based framework for investigating the evolution and resilience of multimodal social networks

A multilayer network-based framework for investigating the evolution and resilience of multimodal social networks

Emergence of a mutual-growth mechanism in networks evolved by social preference based on indirect utility

Preferential attachment is an important mechanism in the structural evolution of complex networks. However, though resources on a network propagate and have an effect beyond a direct relationship, growth by preferential attachment based on indirectly propagated resources has not been systematically investigated. Here, we propose a mathematical model of an evolving network in which preference is proportional to a utility function reflecting direct utility from directly connected nodes and indirect utility from indirectly connected nodes beyond the directly connected nodes. Our analysis showed that preferential attachment involving indirect utility forms a converged and hierarchical structure, thereby significantly increasing the indirect utility across the entire network. Further, we found that the structures are formed by mutual growth between adjacent nodes, which promotes a scaling exponent of 1.5 between the number of indirect and direct links. Lastly, by examining several real networks, we found evidence of mutual growth, especially in social networks. Our findings demonstrate a growth mechanism emerging in evolving networks with preference for indirect utility, and provide a foundation for systematically investigating the role of preference for indirect utility in the structural and functional evolution of large-scale social networks.

Deep Learning for Combating Misinformation in Multicategorical Text Contents.

Currently, one can observe the evolution of social media networks. In particular, humans are faced with the fact that, often, the opinion of an expert is as important and significant as the opinion of a non-expert. It is possible to observe changes and processes in traditional media that reduce the role of a conventional 'editorial office', placing gradual emphasis on the remote work of journalists and forcing increasingly frequent use of online sources rather than actual reporting work. As a result, social media has become an element of state security, as disinformation and fake news produced by malicious actors can manipulate readers, creating unnecessary debate on topics organically irrelevant to society. This causes a cascading effect, fear of citizens, and eventually threats to the state's security. Advanced data sensors and deep machine learning methods have great potential to enable the creation of effective tools for combating the fake news problem. However, these solutions often need better model generalization in the real world due to data deficits. In this paper, we propose an innovative solution involving a committee of classifiers in order to tackle the fake news detection challenge. In that regard, we introduce a diverse set of base models, each independently trained on sub-corpora with unique characteristics. In particular, we use multi-label text category classification, which helps formulate an ensemble. The experiments were conducted on six different benchmark datasets. The results are promising and open the field for further research.

Opinion evolution and dynamic trust-driven consensus model in large-scale group decision-making under incomplete information

The shift to a new era of dealing with big data has driven continuous progress and development in computer science, artificial intelligence and machine learning. This change has led to the application of advanced techniques in the realm of decision science, particularly in the area of large-scale group decision-making (LSGDM). However, although these existing techniques have become the core of LSGDM methods, they are still limited in solving problems facing incomplete data. In addition, due to the rise of social media platforms such as Weibo, WeChat and Twitter, which build bridges for communication between decision makers (DMs), this brings new opportunities and challenges for consensus research. To address this set of issues, this study develops a consensus architecture that combines dynamic social network and opinion evolution in the context of an incomplete multi-attribute LSGDM. It is worth mentioning that the proposed consensus framework is a novel decision-making system that can be used to complete the estimation of the missing values and the consensus reaching process (CRP) by simulating the realistic decision-making scenarios. Firstly, considering the size of the trust value and the length of the path, a new trust propagation method is designed to achieve a more reliable estimation of the unknown trust value. Secondly, this paper establishes a missing value estimation method by virtue of the improved DeGroot model, which is able to obtain complete evaluation information by simulating the opinion formation process of DMs. Next, a hierarchical clustering algorithm with stronger robustness is constructed, which not only can adaptively complete the clustering process, but also integrally considers two attributes of trust and opinion similarity. In light of the above research, this study designs an opinion evolution and dynamic trust-driven consensus model, referred to as the DSN-DG-LSGDM model. Finally, the sensitivity analysis and experiments on a real dataset verify the significant superiority of the constructed DSN-DG-LSGDM model compared with the extant LSGDM consensus models.

The nature and nurture of network evolution

Although the origin of the fat-tail characteristic of the degree distribution in complex networks has been extensively researched, the underlying cause of the degree distribution characteristic across the complete range of degrees remains obscure. Here, we propose an evolution model that incorporates only two factors: the node’s weight, reflecting its innate attractiveness (nature), and the node’s degree, reflecting the external influences (nurture). The proposed model provides a good fit for degree distributions and degree ratio distributions of numerous real-world networks and reproduces their evolution processes. Our results indicate that the nurture factor plays a dominant role in the evolution of social networks. In contrast, the nature factor plays a dominant role in the evolution of non-social networks, suggesting that whether nodes are people determines the dominant factor influencing the evolution of real-world networks.

Social inheritance of avoidances shapes the structure of animal social networks.

Social structure can have significant effects on selection, affecting both individual fitness traits and population-level processes. As such, research into its dynamics and evolution has spiked in the last decade, where theoretical and computational advances in social network analysis have increased our understanding of its ecological and inheritance underpinnings. Yet, the processes that shape the formation of structure within social networks are poorly understood and the role of social avoidances unknown. Social avoidances are an alternate of social affiliation in animal societies, which, although invisible, likely play a role in shaping animal social networks. Assuming social avoidances evolve under similar constraints as affiliative behavior, we extended a previous model of social inheritance of affiliations to investigate the impact of social inheritance of avoidances on social network structure. We modeled avoidances as relationships that individuals can copy from their mothers or from their mother's social environment and varied the degrees to which individuals inherit social affiliates and avoidances to test their combined influence on social network structure. We found that inheriting avoidances via maternal social environments made social networks less dense and more modular, thereby demonstrating how social avoidance can shape the evolution of animal social networks.

THE (NON) INCIDENCE OF ISS – SERVICE TAX ON ACTIVITIES CARRIED OUT ON SOCIAL NETWORKS

With the advent of the internet and the evolution of social networks there have been numerous changes in people's behavior, especially with the emergence of new business formats, one of which is the digital influencer, so much so that the Ministry of Labor included the profession in the Classification Brazilian Occupations. The question is to know which services are provided by the Digital Influencer and whether they are a hypothesis of Service Tax, including the new decision of the Federal Supreme Court that established that the list of services is exhaustive, allowing for an extensive interpretation.

Social Network Evolution of Multiagent Groups in Task Allocation

The social network and group structure can improve the effectiveness of tasks and the management of multiagent systems (MASs). However, in complex task environments, the fixed network and social relation are not necessarily the optimal design. Therefore, agents should be able to adjust social relations in a distributed and self-organized manner to adapt to different task demands. To solve this problem, a hybrid social evolution mechanism based on Q-learning is proposed in this article. The evolution of social relations is realized from the perspective of individual, group, and global situations to improve the success rate and comprehensive effectiveness of task allocation in the system while reducing the communication cost during task execution. In addition, the hybrid evolution mechanism considers the balance of interests between the global and the individual, which can guarantee the rationality of task benefit distribution while improving the global effectiveness. The experiments show that compared with the single-form evolution method, the hybrid mechanism proposed in this article has more advantages in the success rate, communication cost, and comprehensive effectiveness of task allocation. Moreover, the rationality of benefit distribution is also improved, and a strong and harmonious social system is realized.

Characterizing growth in decentralized socio-economic networks through triadic closure-related network motifs

The emergence of the Web3 paradigm has led to more and more systems built on blockchain technology and relying on cryptocurrency tokens – both fungible and non-fungible – to sustain themselves and generate profit. The growth and success of these platforms are strongly dependent on the growth and evolution of the trade relationships among users. In this context, it is of paramount importance to understand the mechanism behind the evolution and growth dynamics of these economic ties: however, in these systems the trade relationships are strictly intertwined with social dynamics, posing significant challenges in the analysis. One of the most important mechanisms behind the evolution of social networks is the triadic closure principle: given the strict link between social and economic spheres, the mechanism emerges as a potential candidate among mechanisms in literature. Therefore in this work, we extend the existing methodology for triadic closure studies and adapt it to directed networks. We performed an analysis centered around 3-node subgraphs known as “triads” and statistically significant triads referred to as “triadic motifs”, both from a static and temporal perspective. The methodology was applied to various decentralized socio-economic networks with distinct levels of social components. These networks include currency transfers from the blockchain-based online social media platform Steemit, trade relationships among NFT sellers and buyers on the Ethereum blockchain, and a blockchain-based currency designed for humanitarian aid called Sarafu. Our measurements show how triadic closure is relevant during the evolution of these platforms and, for a few aspects, more impactful than centralized online social networks, where triadic closure is also incentivized by recommendation systems. Moreover, we are able to highlight both similarities and differences across networks with different levels of social components, both from a static and temporal standpoint. Overall our work presents strong evidence that triadic closure is an important evolutionary mechanism in decentralized socio-economic networks. Our findings provide a stepping stone in the study of decentralized socio-economic networks. Understanding the evolution of other decentralized networks, not following the same Web3 paradigm or with different social components will provide valuable insight into the understanding of dynamics in decentralized systems and potentially improve their design process.