Dynamic Social Networks Research Articles

Long-term cooperative relationships among vampire bats are not strongly predicted by their initial interactions.

In many group-living animals, survival and reproductive success depend on the formation of long-term social bonds, yet it remains largely unclear why particular pairs of groupmates form social bonds and not others. Can social bond formation be reliably predicted from each individual's immediately observable traits and behaviors at first encounter? Or is social bond formation hard to predict due to the impacts of shifting social preferences on social network dynamics? To begin to address these questions, we asked how well long-term cooperative relationships among vampire bats were predicted by how they interacted during their first encounter as introduced strangers. In Study 1, we found that the first 6h of observed interactions among unfamiliar bats co-housed in small cages did not clearly predict the formation of allogrooming or food-sharing relationships over the next 10 months. In Study 2, we found that biologger-tracked first contacts during the first 4-24h together in a flight cage did not strongly predict allogrooming rates over the next 4 months. These results corroborate past evidence that social bonding in vampire bats is not reducible to the individual traits or behaviors observed at first encounter. Put simply, first impressions are overshadowed by future social interactions.

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

Purpose This 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/approach Com_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. Findings Experiments 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 implications Com_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/value Com_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.

Three-Stage Cascade Information Attenuation for Opinion Dynamics in Social Networks.

In social network analysis, entropy quantifies the uncertainty or diversity of opinions, reflecting the complexity of opinion dynamics. To enhance the understanding of how opinions evolve, this study introduces a novel approach to modeling opinion dynamics in social networks by incorporating three-stage cascade information attenuation. Traditional models have often neglected the influence of second- and third-order neighbors and the attenuation of information as it propagates through a network. To correct this oversight, we redefine the interaction weights between individuals, taking into account the distance of opining, bounded confidence, and information attenuation. We propose two models of opinion dynamics using a three-stage cascade mechanism for information transmission, designed for environments with either a single or two subgroups of opinion leaders. These models capture the shifts in opinion distribution and entropy as information propagates and attenuates through the network. Through simulation experiments, we examine the ingredients influencing opinion dynamics. The results demonstrate that an increased presence of opinion leaders, coupled with a higher level of trust from their followers, significantly amplifies their influence. Furthermore, comparative experiments highlight the advantages of our proposed models, including rapid convergence, effective leadership influence, and robustness across different network structures.

DCDIMB: Dynamic Community-based Diversified Influence Maximization using Bridge Nodes

Influence maximization (IM) is the fundamental study of social network analysis. The IM problem finds the top k nodes that have maximum influence in the network. Most of the studies in IM focus on maximizing the number of activated nodes in the static social network. But in real life, social networks are dynamic in nature. This work addresses the diversification of activated nodes in the dynamic social network. This work proposes an objective function that maximizes the number of communities by utilizing bridge nodes. We also propose a diffusion model that considers the role of inactive nodes in influencing a node. We prove the submodularity, and monotonicity of the objective function under the proposed diffusion model. This work analyzes the impact of different ratios of bridge nodes in the seed set on real-world and synthetic datasets. Furthermore, we prove the NP-Hardness of the objective function under the proposed diffusion model. The experiments are conducted on various real-world and synthetic datasets with known and unknown community information. The proposed work experimentally shows that the objective function gives the maximum number of communities considering bridge nodes compared with the benchmark algorithms.

Cuckoo Search Optimization-Based Influence Maximization in Dynamic Social Networks

Online social networks are crucial in propagating information and exerting influence through word-of-mouth transmission. Influence maximization (IM) is the fundamental task in social network analysis to find the group of nodes that maximizes the influence in the social network. IM has different applications like viral marketing, campaigning, advertising, and so on. Literature has presented various algorithms based on different approaches to address the IM problem, including nature-inspired algorithms. Most of the work focuses on the static social network. The proposed work first employs nature-inspired Cuckoo Search Optimization to solve the IM problem in dynamic networks. The proposed algorithm applies the fuzzy-logic-based technique to optimize the nests. We also perform statistical tests to show the effectiveness of the proposed algorithm with the benchmark algorithms. The experimental results are performed on five datasets and compare the results with the state-of-the-art algorithms. The results show that the proposed algorithm gives better results than the nature-inspired state-of-the-art algorithms.

Cognitive dissonance and introversion effects on opinion dynamics and echo chamber formation

Bounded confidence models (BCM) are extensively used to model continuous opinion dynamics in social networks. Typically, these models are analysed on static networks where edges do not vary over time. Following in the footsteps of adaptive voter models, further research has considered BCMs in the setting where agents are able to dynamically adjust their edges, which subsequently feedback into the opinion dynamics of the network. Several methods of updating connections have been proposed ranging from random rewiring to more sophisticated approaches based on concordant edges, homophily and cognitive dissonance. We present a modified form of the bounded confidence model, termed the selfish agent opinion (SAO) model, where connection updates are evaluated using a general cost function. Agents in the SAO model maintain two classes of relationships, friends and acquaintances, based on which they update their opinions and edges to optimise a payoff function that may include multiple social factors. This paper explores the effects, which we describe as “cognitive dissonance” and “introversion” that attempt to mimic postulated behaviours associated with the social concepts of the same name, on social network and opinion dynamics. We find that the SAO model naturally produces echo chambers for social networks with increased sensitivity to cognitive dissonance, whilst introversion produces high levels of fragmentation and low opinion mobility. Additionally, the effect of tolerant agents and inquisitive social encounters is investigated. It is found that both the presence of very small numbers of tolerant agents and inquisitive encounters are able to strongly promote consensus formation.

Timeliness-aware rumor sources identification in community-structured dynamic online social networks

Identifying rumor sources in online social networks (OSNs) plays a crucial role in controlling the spread of rumors and mitigating their damage. However, the community structure of OSNs and timeliness of rumors increase the complexity of accurately characterizing rumor-spreading behavior, making it extremely challenging to identify the source of rumor in OSNs. Conventional studies on rumor source identification often overlook the community structure of OSNs and timeliness of rumors. This paper proposes a rumor sources identification framework that take these two aspects into consideration. First, we transfer the snapshots of a dynamic OSN into a community-structured dynamic OSN through 4 meticulously designed phases. Second, instead of considering a constant influence of rumors in traditional techniques, we introduce a time-varying dynamic propagation parameter to quantify the timeliness of rumor, and apply the propagation parameter to establish a microscopic rumor spreading model. This process addresses the issue of modeling the timeliness of rumor. Third, we adopt sensor-based observation techniques to collect the propagation information of rumor, and a hybrid sensor deployment strategy is designed to improve the efficiency of information collection. Fourth, we propose an algorithm for identifying single and multiple rumor sources in community-structured dynamic OSN, this algorithm initiates with the utilization of some criteria to analyze the gathered propagation information and estimate the suspicious communities that harbor the source of rumors. Subsequently, it applies maximum likelihood estimation method within each community to determine the ultimate source of the rumor. Experimental results on real-world and synthetic networks indicate that our method can accurately identify the real rumor sources. To the best of our knowledge, the proposed method is the first that can be used to identify rumor sources in community-structured dynamic OSNs.

DTM-Based Analysis of Hot Topics and Evolution of China’s Energy Policy

Quantitative research on the evolution and transformation of topics in China’s energy policy can enhance the theoretical and methodological framework of policy document analysis. Utilizing dynamic topic modeling (DTM) and social network analysis, this study examined 1872 energy policy documents issued in China between 1980 and 2023, focusing on detecting hot topics and analyzing trend evolution. DTM identified five core topics: State Grid and new energy, comprehensive energy conservation and emission reduction, intelligent building energy management, promotion of energy-saving products and new energy vehicles, and standardization of energy industry management. Temporal analysis of these core topics reveals a shift in policy focus over time, moving from infrastructure development and standardization management to new energy development and modernization of the energy system. The co-occurrence network of thematic terms transitions from an “independent and loose” structure to a “concentrated and balanced” one, with increasing network scale and frequency. The conclusions of this study offer valuable insights for establishing a dynamic monitoring and real-time updating mechanism for energy policies, enhancing the integration and coordination of energy policy topics, and effectively supporting national energy strategies in response to global energy market challenges.

Context propagation based influence maximization model for dynamic link prediction

Influence maximization (IM) in dynamic social networks is an optimization problem to analyze the changes in social networks for different periods. However, the existing IM methods ignore the context propagation of interaction behaviors among users. Hence, context-based IM in multiplex networks is proposed here. Initially, multiplex networks along with their contextual data are taken as input. Community detection is performed for the network using the Wilcoxon Hypothesized K-Means (WH-KMA) algorithm. From the detected communities, the homogeneous network is used for extracting network topological features, and the heterogeneous networks are used for influence path analysis based on which the node connections are weighted. Then, the influence-path-based features along with contextual features are extracted. These extracted features are given for the link prediction model using the Parametric Probability Theory-based Long Short-Term Memory (PPT-LSTM) model. Finally, from the network graph, the most influencing nodes are identified using the Linear Scaling based Clique (LS-Clique) detection algorithm. The experimental outcomes reveal that the proposed model achieves an enhanced performance.

Effect of Centrality on Physical Activity in Late Childhood: A 1-Year Prospective Cohort Study.

Engaging in physical activity (PA) is crucial for children's physical and mental health, with PA in childhood influencing lifelong activity levels. However, PA during childhood tends to decrease with age. Childhood friendship networks influence various health behaviors, including physical activity. Centralities are objective measures of an individual's position and role in friendship networks. The relationship between centrality and PA is inconsistent. This study aimed to determine how centrality affects changes in PA in late childhood longitudinally and to investigate the distribution of centrality in the network. This prospective cohort study recruited fourth- and fifth-grade children (9-11 years old). A total of 143 children participated. We calculated three centralities-in-degree, closeness, and betweenness-based on social network analysis (SNA). PA was assessed using the physical activity questionnaire for older children (PAQ-C). To explore the relationship between centralities and the percentage change in PA, a multivariate logistic regression analysis was performed. Children with higher closeness had a significantly higher rate of decrease in PA after adjusting for confounding factors. There was no significant association between betweenness and percentage change in PA (p = 0.66) or in-degree and percentage change in PA (p = 0.21). This study highlights the importance of considering social network dynamics in PA interventions, particularly for children with high social closeness. Future research should incorporate objective PA measures and explore broader social networks to enhance intervention strategies, especially for Generation Z and Alpha, who experience unique opportunities and motivations for PA due to pervasive digital environments.

Opinion dynamics in social networks incorporating higher-order interactions

Opinion dynamics in social networks incorporating higher-order interactions

Dynamic remodeling model based on chemotaxis of slime molds.

Social infrastructure networks, essential for daily life and economic activities, encompass utilities such as water, electricity, roads, and telecommunications. Dynamic remodeling of these systems is crucial for responding to continuous changes, unexpected events, and increased demand. This study proposes a new dynamic remodeling model inspired by biological mechanisms, focusing on a model based on the chemotaxis of slime molds. Slime molds adapt spontaneously to environmental changes by remodeling through the growth and degeneration of tubes. This capability can be applied to optimizing and dynamic remodeling social infrastructure networks. This study elucidated the chemotactic response characteristics of slime molds using biological experiments. The mold's response was observed by considering changes in the concentration of chemicals as environmental changes, confirming that slime molds adapt to environmental changes by shortening their periodic cycles. Subsequently, based on this dynamic response, we propose a new dynamic model (oscillated Physarum solver, O-PS) that extends the existing Physarum solver (PS). Numerical simulations demonstrated that the O-PS possesses rapid and efficient path-remodeling capabilities. In particular, within a simplified maze network, the O-PS was confirmed to have the same shortest-path searching ability as the PS, while being capable of faster remodeling. This study offers a new approach for optimizing and dynamically remodeling social infrastructure networks by mimicking biological mechanisms, enabling the rapid identification of solutions considering multiple objectives under complex constraints. Furthermore, the variation in convergence speed with oscillation frequency in the O-PS suggests flexibility in responding to environmental changes. Further research is required to develop more effective remodeling strategies.

Simulation of policies to promote pro‐environmental behavior in a dynamic learning model of social networks: Cases of Brazil, the United States, Japan, Germany, China, and India

AbstractPro‐environmental behavior does not seem to diffuse sufficiently in Brazil, China, Germany, Japan, India, or the United States. Based on the current distribution of carbon footprints and learning patterns (DeGroot learning, best practice, and free riding) in social networks in the six countries, what will happen to the diffusion of pro‐environmental behaviors in each country? What policy measures will be most effective? This study aims to simulate the diffusion of pro‐environmental behavior and to test the effectiveness of various policy measures in a dynamic social network model in the six countries. The distribution of pro‐environmental behaviors is generated by global gridded carbon footprint data. The distribution of learning patterns in social networks is estimated in each country, using data from the World Values Survey. This study considers three policy measures: increasing the number of people pursuing best pro‐environmental behavior practices (best practice promotion, [BPP]), decreasing the number of free riders (free‐riding reduction, [FRR]), and converting the learning patterns of people with high degrees of centrality into best practice (centrality‐based best practice promotion, [CBPP]). Interesting findings were threefold. First, there are large differences in the estimated degrees of individual pro‐environmental behavior and the proportions of learning patterns in the six countries. Second, FRR and BPP are effective in promoting pro‐environmental behavior, but the choice of FRR or BPP depends on the initial proportion of free riders. Third, CBPP is the most effective policy of the three in all countries. Centrality in social networks must be considered when implementing intervention.

Modelling co-evolution of resource feedback and social network dynamics in human-environmental systems

Games with environmental feedback have become a crucial area of study across various scientific domains, modelling the dynamic interplay between human decisions and environmental changes, and highlighting the consequences of our choices on natural resources and biodiversity. In this work, we propose a co-evolutionary model for human-environment systems that incorporates the effects of knowledge feedback and social interaction on the sustainability of common pool resources (CPRs). The model represents consumers as agents who adjust their resource extraction based on the resource’s state. These agents are connected through social networks, where links symbolize either affinity or aversion among them. The interplay between social dynamics and resource dynamics is explored, with the system’s evolution analyzed across various network topologies and initial conditions. We find that knowledge feedback can independently sustain CPRs. However, the impact of social interactions on sustainability is dual-faceted: it can either support or impede sustainability, influenced by the network’s connectivity and heterogeneity. A notable finding is the identification of a critical network mean degree, beyond which a depletion/repletion transition parallels an absorbing/active state transition in social dynamics, i.e. individual agents and their connections are/are not prone to being frozen in their social states. Furthermore, the study examines the evolution of the social network, revealing the emergence of two polarized groups where agents within each community have the same affinity. Finally, we observe an inverse relationship between system complexity and sustainability. Comparative analyses using Monte–Carlo simulations and rate equations are employed, along with analytical arguments, to reinforce the study’s findings. The model successfully captures key aspects of the human-environment system, offering valuable insights to understand how both the spread of information and social dynamics may impact the sustainability of CPRs.

Harmony under threat: does resource-mediated stress affect the (caste-based) social network of leaf-cutting ant colonies?

Managing pest species of eusocial insects, such as leaf-cutting ants, poses significant challenges. Controlling them requires understanding of how toxic plant substrates and ant baits are recognized by foragers, transported to the nest, shared among workers and managed by gardeners cultivating the symbiont fungus garden. Despite this, little is known about how unsuitable resources might impact social interactions within ant colonies. This study aims to investigate whether the provision of a suitable substrate (copperleaf) and a toxic substrate (nasturtium leaves) affects the social network dynamics within colonies of two leaf-cutting ant species: Acromyrmex molestans and Acromyrmex subterraneus. The interactions between castes were recorded and subjected to social network analyses. Initial foraging duration increased for A. subterraneus provided with copperleaf, although no difference was observed for the other species and resource combinations. The social network structure was similar for both species when copperleaf leaves were provided as a substrate. However, notable alterations occurred with nasturtium leaf provision, leading to higher integration of gardeners in interactions and noticeable changes in the generalist worker network centrality, particularly in A. subterraneus. The observed changes in social interactions, particularly in A. subterraneus, suggest that increasing gardener interactions with other castes expedites the movement of the substrate within the colony. This maximizes the potential toxic effect on the colony. © 2024 Society of Chemical Industry.

Social user role value analysis and trusted user autonomous diffusion for participatory crowdsensing

Social participatory-sensing, as an emerging research field, is extremely challenging to recruit trustworthy and active high-value users in a sparse user context. And the richness and closeness of users' social relationships provide new research ideas. Therefore, a participatory sensing user recruitment method based on dynamic social network role detection (DSRD) is proposed, in which firstly, multiple identity information of users is mined based on the decomposition of their overlapping social relationships to filter out high-quality sensing users. Secondly, based on role-oriented network representation learning, it models users' role information and establishes a role hierarchy model to evaluate users' social functions and role values. Finally, the concept of temporal social centrality is proposed for the first time for integrating users' social and network structural features to assess the overall value of users and ensure the coverage of task assignments under a sparse user pool. Experimental results on the open datasets Gowalla and Brightkite show that under the constraints of cost budget and number of users, the proposed user recruitment framework DSRD effectively improves task coverage with less time overhead compared to the baseline algorithm.

Evolution of green travel behaviour on dynamic social networks

Encouraging residents’ green travel behaviour can reduce carbon emissions. However, existing research focuses more on the individual level than on green travel in the context of group interactions. This study aims to connect individual- and group-level insights by integrating the theory of planned behaviour (TPB) and the norm activation model (NAM). Based on empirical data from a questionnaire survey and web crawler, a simulation of the evolution of green travel behaviour interactions in social networks was conducted. The results reveal that the theory of planned behaviour and the norm activation model (TPB −NAM) can explain and predict green travel behaviour. With individuals’ interactions, social networks present scale-free characteristics, and the state of green travel behaviour tends to be stable. Lowering the opinion-bounded confidence of agents in social networks could promote green travel behaviour. This study extends the literature on the theory of green travel behaviour in terms of model integration and interactive decision-making between individuals and groups. The results support the promotion of residents’ green travel behaviour.

When to generalise and when to specialise? Climate change and hominin biocultural adaptability in the African early and middle stone age

A growing number of authors have discussed the role of climate change in periods of important biological and cultural transition along the hominin lineage. This paper establishes a biocultural framework elucidating human behavioural adaptations during the African Early and Middle Stone Age, centred on three crucial dimensions of hunter-gatherer adaptation: mobility, social network dynamics, and technology. We contend that landscape properties, specifically resource diversity and seasonal to inter-annual resource variability, can be used to model the specific responses of hominin groups to climate change over time, based on their awareness of these properties. Specifically, we focus on hominin technological generalisation and specialisation, meaning the extent to which there is a high degree of specificity (or fit) between final tool form and the task(s) in which the tool is deployed.In this regard, we argue that the archaeological record reveals punctuated and discontinuous specialisation during certain phases of the Early Stone Age driven by landscape predictability. These periods encourage the expression of relevant innovations and stepwise increases in technological complexity. While some of them become lost to demographic or cultural stochasticity, others end up forming the basis for a standardisation of generalised forms within the context of unexpected climatic deterioration. This is highlighted by the late Acheulean: following a period of greater generalisation in the late Early Pleistocene correlating with repeated and severe orbitally-forced periods of aridity, smaller biface forms become more common (or absent) and regional experimentation with prepared-core technology in Eastern Africa takes place in the context of a return to more humid and stable climatic conditions. The onset of more arid and variable climates associated with the emergence of the Middle Stone Age led to the continental expansion of the prepared-core technological substrate underpinning generalised assemblages. The cycle continues in the Middle Stone Age with a return to climatic stability in the Late Pleistocene and subsequent regional diversification of this techno-complex, in which hominins responded with greater toolkit specialisation in a number of different ways. In this context, we support the existence of a cyclical and non-linear relationship between environmental adaptation and cognitive evolution, as part of a wider biocultural feedback loop, which contributes to explain the evolutionary roots of our “generalist specialist” niche.

DEVELOPING GAME THEORY-BASED METHODS FOR MODELING INFORMATION CONFRONTATION IN SOCIAL NETWORKS

This paper explores the essential dynamics of social networks, specifically examining the phenomenon of information confrontation among users. The goal of the research is the development of a novel simulation methodology that integrates game-theoretic principles with probabilistic techniques to provide a robust model for these interactions. The theoretical framework of the study is founded on the conceptualization of user conflicts as a strategic game between two players. The primary objective for each player in this game is to exert influence and control over as many nodes within the network as possible. To capture the essence of these strategic interactions, we have introduced an innovative algorithm that facilitates dynamic strategy adaptation. This algorithm is pivotal in allowing players to modify their decision-making processes in real-time, based on the continually changing conditions of the network. For practical implementation and validation of the methodology, we used the Facebook Researcher open dataset, with a particular focus on its Kazakhstani segment. This dataset provides a rich source of empirical data, reflecting diverse user interactions and network configurations, which are essential for testing the model. This approach stands out by offering significant improvements in computational efficiency and resource management. By dynamically tracking and updating the network's status, the proposed method reduces the computational resources required, thereby enhancing the scalability of the simulation. In comparing our methodology with other existing models in the field, it becomes evident that it not only matches but in several respects surpasses these methodologies in terms of flexibility. This study makes substantial contributions to the field of social network analysis by providing a sophisticated tool that can be effectively employed to navigate and analyze the complexities of information confrontation in digital social spaces.

Agent-based modelling of polarized news and opinion dynamics in social networks: a guidance-oriented approach

Abstract In the contemporary social network landscape, opinion polarization has emerged as a prominent challenge, sparking concerns about the effective guidance of news sentiment and mitigation of opposing opinions. This is particularly pertinent in the intricate web of social networks, where complexity reigns supreme. Addressing this pivotal issue, this article introduces a news opinion guidance approach grounded in motif recognition. To accurately mirror real-world social networks, we have crafted an agent-based model that simulates polarized news propagation. This model encompasses diverse media agents and user agents, meticulously replicating the news dissemination process within the network. In our quest to unveil the underlying structures of social networks, we have enhanced the Augmented Multiresolution Network approach, incorporating multi-dimensional node attributes for more nuanced clustering and network mapping. This refinement enables us to pinpoint potential motif regions with greater precision. Leveraging these insights, we introduce a triangular motif-based opinion guidance strategy aimed at shaping opinion distribution by bolstering the influence of nodes within these motifs. Once the pertinent motifs are identified, we undertake simulation experiments that reveal the remarkable efficacy of our motif recognition-driven guidance strategy. Notably, it reduces opinion polarization by a substantial 74% compared to scenarios without guidance strategies. This research offers a fresh perspective on crafting personalized and targeted news sentiment guidance strategies. It presents a versatile and potent computational framework for understanding and managing polarization phenomena in social networks, carrying profound theoretical and practical ramifications.