Interacting Discovery Processes on Complex Networks.

Forecasting Networks Links with Laplace Characteristic and Geographical Information in Complex Networks

PurposeThis study aims to analyze social networking and network centrality in the case of community enterprises, which the existing literature has not addressed. To do so, the authors use the survey of community enterprises from the One Tambon One Product (OTOP) entrepreneurship program of Nong Khai border province in Thailand as a case study.Design/methodology/approachSocial network analysis (SNA) is a tool to study and understand the relationship patterns of units of analysis, which can be individual, household, community or production units, and how those units interact through social or economic activities. Network positions are important when identifying the centralization of links in a particular network. If a representative is close to the center of a network, it is possible that the production unit will be able to contact or coordinate with a greater number of other members of the cluster, create knowledge sharing, promote collaboration and then typically demonstrate greater performance.FindingsThe results show that overall, local government agencies play a critical role in the community enterprise relationship network, while private entities are the least active group. Enterprises in partnerships with external agencies are mostly cooperating with branding and marketing dimensions, followed by the design dimension. There is no cooperation regarding production and distribution. Most community enterprises have established at least a one-dimensional network of cooperation with external agencies; only five community enterprises have isolated nodes with no partnerships having been created within this group.Research limitations/implicationsThe study was limited by the surveys having been conducted in a single area and, therefore, can be used only as a case study for this area. Surveys in larger group sizes and in a wider range of areas would lead to results with greater applicability and reliability.Practical implicationsThese results bring to mind policy proposals to increase the competitiveness of community enterprises through the development of social networks as follows: firstly, knowledge should be created with community enterprise operators to understand their supply chains and analyze the strengths and weaknesses and core competencies of their enterprises; and secondly, enterprises should be educated about which agencies can assist businesses at each stage in the value chain system and encouraged to ask for help in adding value at each stage of production.Social implicationsA “OTOP to Business Networking” platform for community and private enterprises should be created with projects/activities that offer venues to exchange business learning and opportunities. Holding meetings among people in a variety of business sectors may help inspire entrepreneurs to innovate to further their businesses, which can lead to networking businesses conducting activities across community enterprises and the private sector for the mutual benefit of producing merchandise for large customers and markets. Learning systems, production techniques and cooperation should be created, as well as opportunities to increase market share and profitability, if this platform is successful.Originality/valueThis paper is the first study, to the best of the authors’ knowledge, to utilize SNA to examine the use of social networking among community enterprises participating in Thailand’s OTOP entrepreneurship program in Nong Khai province. The results show that overall, local government agencies play a critical role in the community enterprise relationship network, while private entities are the least active group. Therefore, the government can play an important role in helping to develop a network of community enterprises with external entities at each stage of the value chain to enhance the competitiveness of each enterprise.

Objectives. The study aimed to investigate contemporary models, methods, and tools used for analyzing complex social network structures, both on the basis of ready-made solutions in the form of services and software, as well as proprietary applications developed using the Python programming language. Such studies make it possible not only to predict the dynamics of social processes (changes in social attitudes), but also to identify trends in socioeconomic development by monitoring users’ opinions on important economic and social issues, both at the level of individual territorial entities (for example, districts, settlements of small towns, etc.) and wider regions.Methods. Dynamic models and stochastic dynamics analysis methods, which take into account the possibility of self-organization and the presence of memory, are used along with user deanonymization methods and recommendation systems, as well as statistical methods for analyzing profiles in social networks. Numerical modeling methods for analyzing complex networks and processes occurring in them are considered and described in detail. Special attention is paid to data processing in complex network structures using the Python language and its various available libraries.Results. The specifics of the tasks to be solved in the study of complex network structures and their interdisciplinarity associated with the use of methods of system analysis are described in terms of the theory of complex networks, text analytics, and computational linguistics. In particular, the dynamic models of processes observed in complex social network systems, as well as the structural characteristics of such networks and their relationship with the observed dynamic processes including using the theory of constructing dynamic graphs are studied. The use of neural networks to predict the evolution of dynamic processes and structure of complex social systems is investigated. When creating models describing the observed processes, attention is focused on the use of computational linguistics methods to extract knowledge from text messages of users of social networks.Conclusions. Network analysis can be used to structure models of interaction between social units: people, collectives, organizations, etc. Compared with other methods, the network approach has the undeniable advantage of operating with data at different levels of research to ensure its continuity. Since communication in social networks almost entirely consists of text messages and various publications, almost all relevant studies use textual analysis methods in conjunction with machine learning and artificial intelligence technologies. Of these, convolutional neural networks demonstrated the best results. However, the use of support vector and decision tree methods should also be mentioned, since these contributed considerably to accuracy. In addition, statistical methods are used to compile data samples and analyze obtained results.

We study randomized gossip‐based processes in dynamic networks that are motivated by information discovery in large‐scale distributed networks such as peer‐to‐peer and social networks. A well‐studied problem in peer‐to‐peer networks isresource discovery, where the goal for nodes (hosts with IP addresses) is to discover the IP addresses of all other hosts. Also, some of the recent work on self‐stabilization algorithms for P2P/overlay networks proceed via discovery of the complete network. In social networks, nodes (people) discover new nodes through exchanging contacts with their neighbors (friends). In both cases the discovery of new nodes changes the underlying network — new edges are added to the network — and the process continues in the changed network. Rigorously analyzing such dynamic (stochastic) processes in a continuously changing topology remains a challenging problem with obvious applications.This paper studies and analyzes two natural gossip‐based discovery processes. In thepush discoveryortriangulationprocess, each node repeatedly chooses two random neighbors and connects them (i.e., “pushes” their mutual information to each other). In thepull discoveryprocess or thetwo‐hop walk, each node repeatedly requests or “pulls” a random contact from a random neighbor and connects itself to this two‐hop neighbor. Both processes are lightweight in the sense that the amortized work done per node is constant per round, local, and naturally robust due to the inherent randomized nature of gossip.Our main result is an almost‐tight analysis of the time taken for these two randomized processes to converge. We show that in any undirectedn‐node graph both processes takerounds to connect every node to all other nodes with high probability, whereasis a lower bound. We also study the two‐hop walk in directed graphs, and show that it takestime with high probability, and that the worst‐case bound is tight for arbitrary directed graphs, whereas Ω(n2) is a lower bound for strongly connected directed graphs. A key technical challenge that we overcome in our work is the analysis of a randomized process that itself results in a constantly changing network leading to complicated dependencies in every round. We discuss implications of our results and their analysis to discovery problems in P2P networks as well as to evolution in social networks. © 2016 Wiley Periodicals, Inc. Random Struct. Alg., 48, 565–587, 2016

Link prediction problem in social networks has received significant interest in the recent past from the researchers in diverse fields. Understanding and analyzing the present links in the social network or any complex networks either to understand their evolution or to predict the future possible links from the existing network (or links) forms the interesting link prediction problem. Link prediction based on Firefly optimization algorithm is proposed in this paper for social networks. The proposed algorithm is executed on a logical graph similar to social network and tested over real networks taking the benchmark data sets. Experimental values are compared with the other methods existing in the literature. From the comparison we can see that the proposed method performs better in terms of precision over the other methods.

Purpose Social network structures significantly impact business opportunity recognition, but existing evidence reveals that not all social network structures work in favor. This study aims to examine the effects of international entrepreneurs’ network centrality and constraints on opportunity recognition in a host country. Also, it analyzes the contextual mechanism of the home country’s cultural tightness and looseness on the relationships between network structures and opportunity recognition. Design/methodology/approach This study adopts a quantitative research approach, using survey data from 220 international entrepreneurs in China to test the hypotheses through multiple regression analysis. Findings This research finds that international entrepreneurs’ network centrality (not the network constraints) in the host country positively contributes to opportunity recognition. Furthermore, this study suggests that international entrepreneurs’ home country cultural tightness negatively moderates the relationship between network centrality and opportunity recognition. Originality/value This study contributes a novel integrative framework to international entrepreneurship by linking entrepreneurs’ social network structures (centrality and constraint) with their home country cultural tightness. Unlike prior studies that view networks or culture in isolation, this paper demonstrates the cross-level contingency effect between individual embeddedness and societal norms. It responds to recent calls for contextualized models in international opportunity recognition (Pidduck et al., 2024; Wang et al., 2019).

This thesis explores the application of complex systems research to understand the dynamics of the art world, considering art as a complex system and investigating its various components through data-driven methodologies. By studying art as a complex system, we contribute to a systematic understanding of human development and behavior in creative domains. The social network plays a crucial role in the cultural evolution of art, shaping our cultural identity and collective memory. Analyzing network characteristics provides insights into how individual decisions influence collective dynamics and sustain social phenomena. Previous studies have used network models and data analysis to examine the role of network position and connectedness in artistic collaborations, individual success, and the transmission of artistic knowledge. In this thesis, we focus on ballet as a unique art form with a rich historical and social structure. Ballet provides an opportunity to investigate the role of the social network in shaping collective dynamics in performing arts. We present two research articles that examine gender inequalities and the role of social connections on the career success of ballet dancers. The first article investigates the social network structure of ballet creations and its potential impact on gender disparities in leading positions. The second article explores the influence of social connections and prestige on the career trajectories of ballet dancers, using network analysis and centrality metrics to uncover hierarchical stratification within ballet academies. Our research highlights the significance of social dynamics and network effects in understanding complex social phenomena in the art world. It offers insights into gender inequalities and career success in ballet and demonstrates the value of data-centric methodologies in arts research. By generating a unique dataset and applying interdisciplinary approaches, we contribute to the scientific examination of the arts and enhance our understanding of human creativity and cultural heritage. This thesis contributes to the broader goal of fostering diversity, equity, and inclusion within the arts by shedding light on social structures and suggesting potential avenues for change. Lastly, this work underscores the importance of interdisciplinary research in enriching our understanding of human development and behavior in creative domains.

Robustness and Optimization of Complex Networks: Reconstructability, Algorithms and Modeling

Burnout is typically viewed as an individual condition, and no link has been identified between burnout and loneliness. To investigate the association of burnout with loneliness and social network degree and centrality. A survey containing the Maslach Burnout Inventory (MBI), a 3-question loneliness scale, and a social connectivity component was sent to residents in a large urban academic medical center internal medicine residency program. The response rate was 77% (95 of 124 residents). We defined significant burnout as MBI subscores of ≥ 27 for emotional exhaustion (EE), ≥ 10 for depersonalization (DP), or both. This was met by 43 (45%), 47 (49%), and 31 (33%) out of 95 respondents, respectively. Those with significant burnout had higher loneliness scores: 5.6 versus 4.5 for EE (P = .002; OR = 1.50; 95% CI 1.15-1.95); 5.4 versus 4.6 for DP (P = .024; OR = 1.33; 95% CI 1.03-1.71); and 5.8 versus 4.6 for both EE and DP (P = .001; OR = 1.54; 95% CI 1.17-2.02). Rating a larger number of coresidents as closer connections on a 5-point Likert scale was not associated with lower burnout scores. No measures of centrality were associated with burnout scores for EE and/or DP. High personal accomplishment subscores on the MBI did correlate significantly with several measures of centrality. Burnout was associated with loneliness in a dose-dependent fashion. Greater sense of personal accomplishment was associated with greater network centrality.

The burgeoning field of network science has started to examine the fundamentals of multi-genre networks, which include traditional communication networks but also encompass information flow networks, social networks, and potentially other networks as well. The dependencies between these different genres (or types) of networks introduce additional complexities that must be addressed. However, the existence of these different genres can also be a benefit to resilience. In this chapter, Alexander Kott and Tarek Abdelzaher first introduce the notion of complex networks or multi-genre networks, followed by definitions of robustness and resiliencyContents 5.1 Introduction to Complex Networks 68 5.2 Resiliency and Robustness 70 5.3 Fundamental Tradeoffs 72 5.4 Robustness to Topology Modifications 73 5.5 Robustness to Cascaded Resource Failures 75 5.6 Buffering and Resiliency to Function Loss 78 5.7 Resiliency to Input Corruption 80 5.8 Resiliency of Inference 81 References 83in complex networks. The core part of the chapter examines several types of failures and approaches to make complex networks robust and/or resilient to these failures.

Analysis of Turkey’s airport network structure and centrality in the opening-out period after the first wave of COVID-19: A complex network approach

Social animals live in complex and variable socio-ecological environments where individuals adapt their behavior to local conditions. Recently, there have been calls for studies of animal social networks to take account of temporal dynamics in social relationships as these have implications for the spread of information and disease, group cohesion, and the drivers of sociality, and there is evidence that maintaining stable social relationships has fitness benefits. It has recently been recognized that male elephants form strong social bonds with other males. The nature of these relationships, and thus network structure, may vary over time in response to varying environmental conditions and as individuals age. Using social network analysis, we examine the stability of relationships and network centrality in a population of male African elephants. Our results suggest that males may maintain stable social relationships with others over time. Older males show greater stability in network centrality than younger males, suggesting younger males face uncertainty in transitioning to adult society. For elephants, where older individuals function as social repositories of knowledge, maintaining a social network underpinned by older males could be of particular importance.

Increasing urbanisation reduces available habitat and increases human-wildlife interactions, presenting social and ecological challenges for many species; however, flexible generalist species, such as the vervet monkey, Chlorocebus pygerythrus, thrive under these pressures. In the urban mosaic, human-food sources represent clumped, monopolisable food that can increase contest competition. Social network analysis (SNA) is a powerful tool to monitor changes in social structure, yet it has rarely been used to study urban wildlife. Using SNA, we investigated the effect of anthropogenic food and human-wildlife interactions on social cohesion in five vervet monkey groups in urban KwaZulu-Natal, South Africa. Over six months, we conducted group scan samples every 30-min on each group and recorded all humans-vervet monkey interactions during dawn to dusk follows. We analysed the effect of foraging on natural and human-related food sources and human-vervet monkey interactions on social network metrics for grooming and aggression at group (density, clustering coefficient and distance) and individual (eigenvector centrality and degree) levels, using linear mixed models. Anthropogenic food influenced almost all social metrics. At the group level, foraging on anthropogenic food was related to increased density and cohesion in both grooming and aggression networks. At the individual level, increasing anthropogenic food affected high-ranking monkeys most: eigenvector centrality and outdegree in aggression networks increased with rank. Social network analysis can be a useful tool to document urban effects on wildlife groups, and aids our understanding of wildlife behavioural flexibility, a key tool in developing educated and effective management strategies.

“Conjectural” links in complex networks

The benefits of sociality are not equitably shared among members of a social group. The position individuals occupy in social networks is key to maximizing their fitness and contribution to group cohesion, as well as stability in the network structure. Individuals' position in a social network is highly influenced by intra‐group competition and affiliation; therefore, it should be assessed by examining various spatial‐social variables. In this study, we examined the relationship between proximity, grooming and agonistic networks, controlling for genetic relatedness, and the effects of sociodemographic factors on the strength centrality (SC) of these social networks. We combined drone technology and social network analysis to test several hypotheses on 16 feral horse (Equus ferus caballus) groups during three consecutive breeding seasons. Our results show a clear relationship between spatial and social behaviours, suggesting that proximity and grooming are intimately connected in promoting social bonding, and agonism may be a consequence of close proximity between conspecifics. Sociodemographic factors shaped the three network centralities differently. Females and higher‐ranking individuals are more central in spatial networks, whereas younger ones may benefit from higher centrality in affiliative networks. Newly integrated individuals tend to be peripheral on spatial networks and engage more in agonistic behaviours; corroborating that integration into a new group is a costly process for females, ultimately leading to a decreased foaling rate. Individuals in multi‐male groups showed higher SC in proximity, but not in agonistic networks, suggesting that a second male may play a role in decreasing overall intra‐group competition. Our results provide a step forward in understanding the costs and benefits of network centrality in non‐matrilineal societies and mechanisms leading to social cohesion, namely in populations under the constant threat of predation and human pressures.