Complex Social Networks Research Articles

A network model of legal relations.

From at least the early twentieth century, legal scholars have recognized that rights and other legal relations inhere between individual legal actors, forming a vast and complex social network. Yet, no legal scholar has used the mathematical machinery of network theory to formalize these relationships. Here, we propose the first such approach by modelling a rudimentary, static set of real property relations using network theory. Then, we apply our toy model to measure the level of modularity-essentially, the community structure-among aggregations of these real property relations and associated actors. In so doing, we show that even for a very basic set of relations and actors, law may employ modular structures to manage complexity. Property, torts, contracts, intellectual property, and other areas of the law arguably reduce information costs in similar, quantifiable ways by chopping up the world of interactions between parties into manageable modules that are semi-autonomous. We also posit that our network science approach to jurisprudential issues can be adapted to quantify many other important aspects of legal systems. This article is part of the theme issue 'A complexity science approach to law and governance'.

Do manager networks affect the efficiency of capital markets? Empirical research based on social network theory

Managers generally possess complex social networks and occupy central positions within their networks. We investigate the influence of managers’ social network relationships on capital market pricing efficiency by specifically analysing the relationship between top management team network centrality and stock price synchronicity. Using stock price synchronicity as a proxy for information efficiency, we examine data from Chinese A-share listed companies over a period of ten years (2013–2022). Based on the social network perspective, we empirically test the relationship between top management team network centrality and stock price synchronicity. Our results reveal that top management team network centrality promotes stock price synchronicity, which is more significant for non-state-owned enterprises. Mechanism testing indicates that analyst attention plays a partial mediating role between top management team network centrality and stock price synchronicity based on herd behaviour and social network theory. Additionally, institutional investors’ shareholding ratios weaken the impact of top management team network centrality, whereas analyst herding strengthens the promotion of stock price synchronicity. After excluding the endogenous effects these main findings remain robust. Overall, the results support the idea that executive networks promote stock price synchronicity.

Collaborative working between speech and language therapists and teaching staff in mainstream UK primary schools: A scoping review

Support for school-age children with speech, language and communication needs (SLCN) usually takes place within the school setting. Successful outcomes for children with SLCN rely on effective collaborative working between speech and language therapists (SLTs), school staff and families. We need to understand the current evidence regarding the joint working practices, relationships and collaboration experiences of SLT and teaching staff within mainstream primary schools, in order to identify whether sufficient research exists for a systematic review within this field, and to inform practice. The purpose of this scoping review was to identify what research currently exists regarding collaboration, roles and relationships of SLTs and teaching staff within mainstream UK primary schools, and clarify the nature, participants and concepts described within this literature. A scoping review framework was used, consisting of identification of the review objectives, identification of relevant studies, study selection and iterative searches, data charting and reporting of the results. Information regarding research question, participants, data collection and analysis and terms used for key concepts was extracted. This scoping review identified 14 papers, however, collaboration was the primary focus of only 5 of these. Clarity and perceptions of roles were key themes within six of the papers. Whilst facilitators and barriers to collaboration are discussed in all 14 papers, only 4 studies aimed to investigate barriers and facilitators. Teaching assistant (TA) views are underrepresented within the research. Drawing conclusions from the body of research is challenging due to the varied ways in which the key concept ‘collaboration’ is used. Currently, there is insufficient literature to carry out a systematic review. This scoping review highlights the need for research that considers collaboration within the complex social network of school staff (including TAs) and SLTs, in order to ensure that future guidance is rooted in research.

AI for crisis decisions

Increasingly, our cities are confronted with crises. Fuelled by climate change and a loss of biodiversity, increasing inequalities and fragmentation, challenges range from social unrest and outbursts of violence to heatwaves, torrential rainfall, or epidemics. As crises require rapid interventions that overwhelm human decision-making capacity, AI has been portrayed as a potential avenue to support or even automate decision-making. In this paper, I analyse the specific challenges of AI in urban crisis management as an example and test case for many super wicked decision problems. These super wicked problems are characterised by a coincidence of great complexity and urgency. I will argue that from this combination, specific challenges arise that are only partially covered in the current guidelines and standards around trustworthy or human-centered AI. By following a decision-centric perspective, I argue that to solve urgent crisis problems, the context, capacities, and networks need to be addressed. AI for crisis response needs to follow dedicated design principles that ensure (i) human control in complex social networks, where many humans interact with AI; (ii) principled design that considers core principles of crisis response such as solidarity and humanity; (iii) designing for the most vulnerable. As such this paper is meant to inspire researchers, AI developers and practitioners in the space of AI for (urban) crisis response – and other urgent and complex problems that urban planners are confronted with.

Dynamics of momentum in financial markets based on the information diffusion in complex social networks

This paper focuses on why momentum expresses different dynamics based on complex social networks. We construct an epidemiological information transmission model under the assumption of complex investor networks. We consider two kinds of networks with different degree distributions: uniform distribution and power-law distribution, and then we discuss the scenarios when the networks are assortative or disassortative. We find that the degree distribution and the degree correlation affect the momentum dynamics. In power-law networks, the assortative network exhibits a lower information diffusion rate in the short term but higher in the long term compared to the disassortative network. In contrast, the disassortative network has a higher information diffusion rate than the assortative network in uniform networks. In power-law networks, network assortativity exerts a significant influence on profit, whereas, in uniform networks, it has minimal impact on profit.

Social network learning: Uncertainty, heterogeneity, and the application in principal–agent relationships

This paper employs algebraic transformation to describe complex social network learning (SNL) behaviors under continuous expected payoff. Three distinct algorithms are then introduced that factor in uncertainty and heterogeneity. We find that individuals' strategies tend to converge through SNL. We then construct a framework for studying the convergence process in the principal–agent relationship by applying our SNL algorithms to distinct scenarios. Our results show that network topology plays a significant role in changes in the payoffs and the convergence speed of individuals' strategies. We also evaluate the impacts of uncertainty, heterogeneity, agents' output efficiency and risk aversion, and individual's centrality on the effectiveness of SNL.

Latin <XS>: Seeing double

Abstract We can observe a systematic reorganization of the Latin Spelling System in the II and I centuries BC. We will focus on the etiology and diffusion of <XS> spelling for the /ks/ cluster instead of <X>, highlighting the interplay between centripetal and centrifugal forces in the standardization process. Its etiology and further diffusion will be compared to the emergence of other spelling innovations in the Latin writing system of the second century BC. We will propose that the progressive integration of <XS> spelling in the systematic orthography of Latin was favored by both morpho-graphemic and phono-graphemic factors. Moreover, we will take into consideration the complex social network of the Roman Republic to trace back <XS> diffusion path. We will propose that, alongside the leading role of the urban elites in its diffusion, also local communities independently developed such a spelling variant, probably as a consequence of Greek systematic orthography’s influence.

The Contribution of Social Networks to Marketing Strategies

It has existed since ancient times, and sociology has conducted in-depth research on it. Sociology believes that society is not composed of individuals but of networks, which contain nodes and their relationships. It can also be understood that our social relationships are intertwined, forming a complex social network. In recent decades, social network analysis has been widely concerned and applied in academic research and practical applications. By analyzing and measuring network graph, it reveals key nodes in social networks, information transmission channels and community structures in networks. Since social networks have a significant impact on the operation and efficiency of the market, this tool is gradually being applied to market research. This paper will focus on three types of contributions of the network to marketing from the perspective of sellers. This paper will show different network diagrams, and describe and analyze these diagrams to reach the corresponding conclusion, that is, the contribution of the network to marketing.

A Emergência da Família Chaple à Elite Liberal: Advogados e Mobilidade Socialna Cuba de Meados do Século XIX

As migrant families relocated to Cuba during the second half of the eighteenth century, new generations of creoles were born during the economic and demographic expansion of the island. Some of these Cubans, inspired on early liberal ideas, found the ways to amass a fortune, and started climbing the social ladder by procuring positions in the local bureaucracy. A legal degree, however, was essential to obtain a high ranked public office, and many new students of law enrolled at the University of Havana. As a result, the number of lawyers on the island multiplied and started occupying posts in the local judiciary and the administration, as well as the academia, to legitimize themselves in the Cuban society. This study thus uses the Chaple family to examine a case of social mobility in early nineteenth century Cuba tied to the increase of the number of legal professionals, and their multiple mechanisms to gain participation and control of the public sphere. As the family Chaple illustrates, legal professionals, with their growing connections, created a complex social, political, and familiar network that included many individuals who held key judicial and administrative positions on the island. These lawyers, who came from humble origins, transformed the colonial society by breaking traditional barriers to the legal education and practice. Consequently, creoles with law degrees, who increasingly participated in local public affairs, emerged as a first generation of Cuban liberal elites during the nineteenth century.

Analysis of Academic Social Networks in Indonesia

Social network analysis to detect communities in social networks is a complex problem, this is due to differences in community definitions and the complexity of social networks. One of the social networks for researchers is the academic social network (ASN). We define the relationships between nodes in ASN into two forms, namely interconnection relationships and interaction relationships. Interconnection relationships are researchers' social relationships that are formed from similarities in discipline between researchers, while interaction relationships are researchers' social relationships that are formed through interactions carried out regarding joint article publications. This research aims to measure the social interactions and social interconnections of researchers in Indonesia using the social network analysis method. The ASN data used in this research comes from the academic social network Researchgate. This research produces information on the social networks of scientific groups in Indonesia and a framework for analyzing researchers' social networks using dual identification community mode which has been able to find and understand the structure of the research community based on records of interactions and interconnections with ASN with similarity values in both forms of network connections 85.9%.

Abstract cognitive maps of social network structure aid adaptive inference.

Social navigation-such as anticipating where gossip may spread, or identifying which acquaintances can help land a job-relies on knowing how people are connected within their larger social communities. Problematically, for most social networks, the space of possible relationships is too vast to observe and memorize. Indeed, people's knowledge of these social relations is well known to be biased and error-prone. Here, we reveal that these biased representations reflect a fundamental computation that abstracts over individual relationships to enable principled inferences about unseen relationships. We propose a theory of network representation that explains how people learn inferential cognitive maps of social relations from direct observation, what kinds of knowledge structures emerge as a consequence, and why it can be beneficial to encode systematic biases into social cognitive maps. Leveraging simulations, laboratory experiments, and "field data" from a real-world network, we find that people abstract observations of direct relations (e.g., friends) into inferences of multistep relations (e.g., friends-of-friends). This multistep abstraction mechanism enables people to discover and represent complex social network structure, affording adaptive inferences across a variety of contexts, including friendship, trust, and advice-giving. Moreover, this multistep abstraction mechanism unifies a variety of otherwise puzzling empirical observations about social behavior. Our proposal generalizes the theory of cognitive maps to the fundamental computational problem of social inference, presenting a powerful framework for understanding the workings of a predictive mind operating within a complex social world.

The Impact of Dissonant Tie on Innovation Performance of Digital Transformation

The acquisition of innovation performance through social network relationship resources is a common behavior pattern of organizational members. Recent social network research suggests that negative ties may also have a positive impact on organizational innovation compared with positive ties. Based on this, the paper investigates the impact of dissonant tie, which are a combination of problem-solving tie and difficult working tie, on organizational innovation. The empirical results show that dissonant tie promotes organizational innovation performance and are enhanced by digital sensing and digital seizing, while the increasing effect of the dissonant tie on organizational innovation performance is not verified under the moderating effect of digital reconfiguring. The findings are useful for understanding why certain negative ties may promote organizational innovation and performance, and to provide a theoretical basis for how manufacturing companies can use complex social network ties to survive organizational change and enhance organizational adaptability in digital transformation.

Ethnographic collections in Northern Ireland and the Solomon Islands tomako (canoe) at the Ulster Museum, 1898–2023

AbstractThe World Cultures collection at National Museums Northern Ireland is an essential source for the study of Irish collecting in the wider British Empire. The 2022 redisplay of the collection in the Ulster Museum's exhibition, Inclusive Global Histories, is part of a staged engagement with local and source communities. Given the critical importance of the global museum decolonisation work of which the exhibition is an example, a fresh consideration of this ethnographic collection's history is timely. This article reviews the collection within the context of the three museums that have housed it, and investigates how curators within the institution understood, represented and displayed the collection. It does so through a case study of a war canoe (tomako), that was taken from the Solomon Islands, by John Casement, a captain in the Royal Navy, and is the largest and among the most significant items within the collection. The canoe's centrality to the gallery — built around it in 1925 — that now contains Inclusive Global Histories reveals complex social networks between nineteenth- and twentieth-century collectors, curators and photographers, and aids understanding of how global human cultures have been regarded in Northern Ireland's civic life.

The Impact of Cultural Norms on Sustainable Entrepreneurship Practices in SMEs of Bangladesh

The objective of this study is to examine the significant impact of cultural norms on sustainable entrepreneurship initiatives in the context of small and medium-sized firms in Bangladesh's dynamic business environment. Utilizing a rigorous research technique, this study conducts semi-structured interviews with a diverse sample of 40 small and medium-sized enterprise owners, executives, and important stakeholders. Following this, a rigorous thematic analysis is conducted, revealing significant themes and complex patterns present in the collected data. The findings reveal a detailed viewpoint, explaining that cultural norms, including factors such as tendencies towards risk avoidance, complex social networks, and the significant impact of family dynamics on business operations, have both positive and negative effects on sustainable entrepreneurship practices in small and medium-sized enterprises. Furthermore, the identified research highlights the utmost importance of governmental policies and regulatory frameworks as influential factors that shape the dimensions of sustainable entrepreneurship. Based on these findings, the research emphasizes the necessity of adopting a comprehensive and integrated strategy that effectively addresses and overcomes the deeply ingrained obstacles rooted in culture. This approach should also prioritize the establishment of a supportive environment, facilitated by well-informed governmental policies and carefully crafted regulations. The implications of these findings have significant resonance beyond the confines of Bangladesh, extending to other developing countries where cultural norms and government regulations can pose substantial obstacles to the achievement of sustainable entrepreneurship. Future research attempts may benefit from delving deeper into the complex relationship between cultural norms and sustainable entrepreneurship practices across various contextual settings.

Anthropological Sociology Of Sharia Economic Law On The Practice Of Gold Arisan Contracts Of Indonesian Sharia Pawnshops

This research analyzes in depth the practice of gold gathering at Sharia Pawnshop from the perspective of sociology, anthropology and sharia economic law to reveal the complex dynamics in modern sharia economic practices and their impact on wider society. This understanding also provides insight into how local traditions and global norms interact in increasingly complex contexts. The author uses qualitative research methods and secondary data analysis. These data will be analyzed using sociological and anthropological theoretical approaches to sharia economic law. The results of this research are that the Gold Arisan practice at Sharia Pawnshop is a collective system of buying gold like traditional arisan. It builds social relationships, reflects cultural values, and adheres to sharia economic principles without usury. Sharia economic laws such as Qardh, Murabahah, and Rahn play an important role. Sociologically, this forms a complex social network. From an anthropological perspective, it reflects the values ​​and traditions of society. From the perspective of economic sharia law, comply with strict principles including the prohibition of usury and asset management. This analysis provides an in-depth understanding of financial practices in the context of culture, social norms and Islamic law, as well as the impact of social interactions.

Dynamic community detection based on graph convolutional networks and contrastive learning

With the continuous development of technology and networks, real-life interactions are gradually being abstracted into social networks for study. Social circles are a fundamental structural feature that is prevalent on social networks. Thus, exploring social circle structure plays an important role in revealing the characteristics of complex social networks and provides guidance for understanding social behavior in real life. For example, it can aid in precision marketing, personalized recommendation, and knowledge dissemination within social circles. One of the important means of identifying social network circles lies on community detection algorithms. However, real-world social networks are often dynamic and can be studied and analyzed by building dynamic networks, while existing dynamic network community detection methods tend to ignore the global structure information and time-series information of nodes. To address this problem, this paper proposes a dynamic network community detection algorithm based on graph convolutional neural networks and contrastive learning, which fully captures the adjacent characteristics between nodes based on the correlation information and leverages the feature smoothing strategy to efficiently extract node representations of dynamic networks under unsupervised scenario. Specifically, the proposed algorithm first utilizes node correlation based aggregation strategy to compute the feature matrix for single time-step of the dynamic network. Then, mutual information maximization is implemented based on cross-entropy between learned local and global representations. To reduce the computational overhead in the optimization process, an additional LSTM module is further equipped for updating the parameters of graph convolutional networks in each time-step. Additionally, a contrastive learning based network smoothing strategy is designed to minimize the feature differences between neighboring nodes. Comparative experiments demonstrate that the proposed algorithm achieves excellent performance on both synthetic and real networks.

Efficient Data Transfer by Evaluating Closeness Centrality for Dynamic Social Complex Network-Inspired Routing

Social Complex Networks in communication networks are pivotal for comprehending the impact of human-like interactions on information flow and communication efficiency. These networks replicate social behavior patterns in the digital realm by modeling device interactions, considering friendship, influence, and information-sharing frequency. A key challenge in communication networks is their dynamic topologies, driven by dynamic user behaviors, fluctuating traffic patterns, and scalability needs. Analyzing these changes is essential for optimizing routing and enhancing the user experience. This paper introduces a network model tailored for Opportunistic Networks, characterized by intermittent device connections and disconnections, resulting in sporadic connectivity. The model analyzes node behavior, extracts vital properties, and ranks nodes by influence. Furthermore, it explores the evolution of node connections over time, gaining insights into changing roles and their impact on data exchange. Real-world datasets validate the model’s effectiveness. Applying it enables the development of refined routing protocols based on dynamic influence rankings. This approach fosters more efficient, adaptive communication systems that dynamically respond to evolving network conditions and user behaviors.

Research on the collaborative evolution process of information in public health emergencies based on complex adaptive system theory and social network analysis: a case study of the COVID-19 pandemic.

This review aimed to elucidate the significance of information collaboration in the prevention and control of public health emergencies, and its evolutionary pathway guided by the theory of complex adaptive systems. The study employed time-slicing techniques and social network analysis to translate the dynamic evolution of information collaboration into a stage-based static representation. Data were collected from January to April 2020, focusing on the COVID-19 pandemic. Python was used to amass data from diverse sources including government portals, public commentary, social organizations, market updates, and healthcare institutions. Post data collection, the structures, collaboration objectives, and participating entities within each time slice were explored using social network analysis. The findings suggest that the law of evolution for information collaboration in public health emergencies primarily starts with small-scale collaboration, grows to full-scale in the middle phase, and then reverts to small-scale in the final phase. The network's complexity increases initially and then gradually decreases, mirroring changes in collaboration tasks, objectives, and strategies. The dynamic pattern of information collaboration highlighted in this study offers valuable insights for enhancing emergency management capabilities. Recognizing the evolving nature of information collaboration can significantly improve information processing efficiency during public health crises.

Agents Attraction Competition in an Extended Friedkin-Johnsen Social Network

Unlike many complex social networks investigated in the existing literatures concerning about the communication, consensus, or cooperative behaviors, this paper is devoted to an agents attraction competition problem in an extended Friedkin-Johnsen network. In this problem, two strategic agents compete to attract the nonstrategic agents to adopt opinions as closer to theirs as possible, by connecting with these nonstrategic agents and allocating their strengths on these connections. Therefore a 2-person zero-sum competitive game is characterized. We develop mathematical tools to provide some useful properties of the game, including the partial derivatives and positive definiteness of the payoff functions, based on which the Nash equilibrium of the game is analyzed. We rigorously examine that each player will allocate their strengths according to the susceptibilities, the intrinsic opinions and the costs of the nonstrategic agents, and the weighted column-sums of the row-stochastic matrix which describes the interpersonal influences of the network, if the network satisfies a condition. Otherwise, a Nash equilibrium seeking algorithm is proposed with simplex constraints, by which each strategic agent can asymptotically learn its optimal strategy. Finally, numerical examples are presented to illustrate the validity of the obtained results.

MFCSNet: A Musician–Follower Complex Social Network for Measuring Musical Influence

Music, a significant and exquisite part of human culture, owns abundant features and enjoys a long-standing history. Music evolves in society over time, while artists’ music gets influenced by personal experiences, external events, and inspirations from predecessors. In this paper, we propose a model named MFCSNet that measures musical influence by utilizing the data sets of musical characteristics and links between music influencers and followers. MFCSNet applies multiple indicators and has more analysis perspectives, and well reflects the influence of different kinds of music in various fields. Firstly, we analyze the influencer–follower relations by looking at the network of musical influence, observing the correlation between followers and influencers, and closely examining several sub-networks extracted from the entire network. Secondly, we propose measures that quantify the similarities within and between musical genres, using musical characteristics, such as danceability, energy, and valence, in order to measure the influence between artists and find the more influential characteristics. Furthermore, we apply MFCSNet on the whole timeline to analyze the evolutions and revolutions of music through time, with the goal of revealing the relation between music and culture, society, politics, and technologies.