Social network analysis has provided important insight into many population processes in wild animals. Constructing social networks requires quantifying the relationship between each pair of ...individuals in the population. Researchers often use association indices to convert observations into a measure of propensity for individuals to be seen together. At its simplest, this measure is just the probability of observing both individuals together given that one has been seen (the simple ratio index). However, this probability becomes more challenging to calculate if the detection rate for individuals is imperfect. We first evaluate the performance of existing association indices at estimating true association rates under scenarios where (1) only a proportion of all groups are observed (group location errors), (2) not all individuals are observed despite being present (individual location errors), and (3) a combination of the two. Commonly used methods aimed at dealing with incomplete observations perform poorly because they are based on arbitrary observation probabilities. We therefore derive complete indices that can be calibrated for the different types of incomplete observations to generate accurate estimates of association rates. These are provided in an R package that readily interfaces with existing routines. We conclude that using calibration data is an important step when constructing animal social networks, and that in their absence, researchers should use a simple estimator and explicitly consider the impact of this on their findings.
•Association indices are widely used to describe relationships between individuals.•Indices include built-in assumptions about how animals are observed.•These assumptions are rarely considered in studies of animal social networks.•We outline limitations and provide new indices that accurately correct for biases.
The ecological conservation and high-quality development of China's Yellow River Basin is a national strategy proposed in 2019. Under China's goal of achieving a carbon peak by 2030 and carbon ...neutrality by 2060, clarifying the carbon footprint of each province and the transfer paths of embodied carbon emissions is crucial to the carbon reduction strategy for this region. This paper uses input-output model and multi-regional input-output model to account for the carbon footprint of nine provinces in the Yellow River Basin, and to estimate the amount of embodied carbon transfer between provinces and industrial sectors. Social network analysis is applied to identify the critical industries in the inter-provincial embodied carbon emission transfers from the three major industries. We found that the per capita carbon footprint of the Yellow River Basin decreased by 23.4% in 2017 compared to 2012. Among the sectoral composition of the carbon footprint of each province, “Processing and manufacturing of petroleum, coking, nuclear fuel, and chemical products”, “Construction”, “Other services”, and “Metal processing and metal, non-metallic products” are the four sectors with a higher proportion of emissions. The embodied carbon emission transfer between the provinces in middle and lower reaches of the Yellow River Basin is much higher than that between the upstream provinces. Among carbon emission transfer network of three major industries in nine provinces，the secondary industry in Shaanxi has the highest centrality and is the most critical industry. This study provides a theoretical basis and data support for formulating carbon emission reduction plans in the Yellow River Basin.
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•Calculate carbon footprint of each sector in the 9 provinces of the Yellow River Basin•Estimate the embodied carbon emission transfer between sectors in the 9 provinces•Construction of inter-provincial embodied carbon emission transfer networks•Identify the critical industries for the embodied carbon emission transfer network
Recent years have seen a resurgence of interest in the relation between networks and spatial context. This review examines critically a selection of the literature on how physical space affects the ...formation of social ties. Different aspects of this question have been a feature in network analysis, neighborhood research, geography, organizational science, architecture and design, and urban planning. Focusing primarily on work at the meso- and microlevels of analysis, we pay special attention to studies examining spatial processes in neighborhood and organizational contexts. We argue that spatial context plays a role in the formation of social ties through at least three mechanisms, spatial propinquity, spatial composition, and spatial configuration; that fully capturing the role of spatial context will require multiple disciplinary perspectives and both qualitative and quantitative research; and that both methodological and conceptual questions central to the role of space in networks remain to be answered. We conclude by identifying major challenges in this work and proposing areas for future research.
Clarifying the spatial association network of provincial building carbon emissions and its influential drivers is profoundly significant for transregional collaborative emission reduction and ...regionally-coordinated development. This study adopts the social network analysis method to investigate the network structure characteristics of carbon emissions in the building sector based on China's provincial-level evidence from 2000 to 2018. Then, the quadratic assignment procedure is further utilized to examine the driving factors. The results demonstrate that building carbon emissions in China take the form of a network structure. From 2000 to 2018, the relevance and stability of the spatial associations gradually strengthened. Shanghai, Jiangsu, Tianjin, Beijing and Zhejiang are in the center of the spatial association network and play a vital part in the network. The network of carbon emissions in the building sector can be classified into four plates: the main inflow plate, main outflow plate, bidirectional spillover plate and agent plate. Geographical adjacency, economic development level, energy intensity and industrial structure are significantly correlated with building carbon emissions. The urbanization level has no significant influence on the spatial correlations of building carbon emissions. This study is conducive to formulating energy conservation policies and promoting transregional collaborative emission reductions.
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•Adopting the SNA method to explore the spatial network structure of China's building carbon emissions.•Using the QAP method to determine the factors affecting the spatial correlation network of building carbon emissions.•The relevance and stability of the spatial associations gradually strengthened.•Shanghai, Jiangsu, Tianjin, Beijing and Zhejiang occupy a central position in the network.•Spatial adjacency, economic development, energy intensity and industrial structure significantly affected spatial network.
Large scale group decision making (LGDM) problems and social network analysis (SNA) methods are both attracting increasing attention, and SNA methods are useful for addressing LGDM problems. By ...considering social network information, a new interval type-2 fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) model is proposed to solve LGDM problems in complex and uncertain environments. First, a SNA community detection method is applied to reduce the complexity of large scale decision makers (DMs) according to the social connections among them. Then, interval type-2 fuzzy sets (IT2 FSs) and linguistic variables are employed to handle the uncertainties, and the TOPSIS method is improved using IT2 FSs to obtain an optimal alternative for a LGDM problem. Next, decision weights are computed based on the centrality of the SNA, and the decision information is aggregated by the interval type-2 fuzzy weighted average method. The procedure for solving LGDM problems is presented. Finally, an illustrative example is investigated to demonstrate the feasibility of the proposed solution for LGDM problems, and the results are compared with those of an existing method to verify the validity of the new proposed method.
China faces enormous pressure to reduce carbon emissions. Since the agglomeration and driving effect of urban agglomerations have continued to increase, relying on the network relationship within ...urban agglomerations to coordinate emission reduction becomes an effective way. This paper combines the modified Gravity model and Social Network Analysis method to measure the structure characteristics of carbon emission spatial correlation network of the seven urban agglomerations as a whole and each urban agglomeration in China, analyzes the interaction mechanism between cities and between urban agglomerations, and finally explores the influencing factors of carbon emission spatial correlation through the QAP analysis method. The results are as follows: (1) As for the overall network, overall scale was increasing, but the hierarchical structure had a certain firmness. YRD and PRD urban agglomerations were at the center of the network and received the spillover relationship of MRYR, CC, CP, and HC urban agglomerations. (2) As for the networks of urban agglomerations, the allocation of low-carbon resource elements still needed to be optimized, especially BTH urban agglomeration. Beijing, Shanghai, Nanjing, Wuxi, etc. were at the center of the network. The influencing factors and degree of carbon emission spatial correlation in each urban agglomeration were different.
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•Carbon emission spatial network of 7 urban agglomerations as a whole is analyzed.•The gravity model is modified taking into account economic factors.•The modified Gravity model and SNA are used to the CO2 study in urban agglomeration.•YRD and PRD urban agglomerations were at the center of the overall network.•The hierarchical structure of the overall network had a certain firmness.
•This article summarises the main challenges in the steps of the social media analytics process.•Possible solutions for these challenges are proposed.•We extend the social media analytics framework ...with challenges in the given phases.
Since an ever-increasing part of the population makes use of social media in their day-to-day lives, social media data is being analysed in many different disciplines. The social media analytics process involves four distinct steps, data discovery, collection, preparation, and analysis. While there is a great deal of literature on the challenges and difficulties involving specific data analysis methods, there hardly exists research on the stages of data discovery, collection, and preparation. To address this gap, we conducted an extended and structured literature analysis through which we identified challenges addressed and solutions proposed. The literature search revealed that the volume of data was most often cited as a challenge by researchers. In contrast, other categories have received less attention. Based on the results of the literature search, we discuss the most important challenges for researchers and present potential solutions. The findings are used to extend an existing framework on social media analytics. The article provides benefits for researchers and practitioners who wish to collect and analyse social media data.
China has released its ambitious target for carbon neutrality by 2060. With decades of top-down energy conservation and pollutant mitigation policies, the techno-mitigation space has gradually ...shrunk, while more mitigation space is required for a systematic approach. To help to uncover CO2 mitigation effects, location and better pathways from a systematic perspective, this paper combines disparity analysis and social network analysis to investigate the synergistic emissions reduction effect of urban agglomerations in three representative Chinese urban agglomerations, namely the Yangtze River Delta urban agglomeration (YRD), Chengdu-Chongqing urban agglomeration (CY) and Guangdong-Hong Kong-Macao urban agglomeration (GHM). Based on understanding of the carbon emission disparity characteristics of the three urban agglomerations using disparity analysis, this study uses social network analysis to study the synergistic CO2 reductions in each urban agglomeration from three perspectives: overall, individual, and connection. The findings emphasize that CY presented the greatest synergistic development capacity but with weak driving ability, indicating that overall synergistic emission reduction was difficult to achieve in a short period. GHM presented obvious fragmentation between the core and peripheral cities, resulting in a weak synergistic mitigation effect. YRD highlighted a solid synergistic development capacity with strong driving ability by its developed cities, thus generating the greatest potential to reduce CO2 emissions in the short and middle terms. Different cities assume different roles in synergistic CO2 reduction. Our results can be expected to enlighten more regionally oriented CO2 mitigation policy implications from an urban agglomeration perspective.
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•SNA and disparity analysis were combined, and their results were integrated and compared.•The problems of synergistic CO2 reduction in three urban agglomerations were identified.•The CO2 mitigation effects, location and better pathways from a systematic perspective were explored.•Cities that dominated the improvement in urban agglomeration synergy were analyzed.•Connection centrality was innovatively used in carbon emission networks.
•Up-to-date literature review of basic research and application domains in social networks.•Definition of a new set of metrics to measure the capacity of SNA frameworks and tools.•Quantitative ...analysis of social network analysis tools and frameworks (SNA).•Evaluation of 20 popular SNA software tools according to the new set of metrics.•SNA software technology assessment.
Social network based applications have experienced exponential growth in recent years. One of the reasons for this rise is that this application domain offers a particularly fertile place to test and develop the most advanced computational techniques to extract valuable information from the Web. The main contribution of this work is three-fold: (1) we provide an up-to-date literature review of the state of the art on social network analysis (SNA); (2) we propose a set of new metrics based on four essential features (or dimensions) in SNA; (3) finally, we provide a quantitative analysis of a set of popular SNA tools and frameworks. We have also performed a scientometric study to detect the most active research areas and application domains in this area. This work proposes the definition of four different dimensions, namely Pattern & Knowledge discovery, Information Fusion & Integration, Scalability, and Visualization, which are used to define a set of new metrics (termed degrees) in order to evaluate the different software tools and frameworks of SNA (a set of 20 SNA-software tools are analyzed and ranked following previous metrics). These dimensions, together with the defined degrees, allow evaluating and measure the maturity of social network technologies, looking for both a quantitative assessment of them, as to shed light to the challenges and future trends in this active area.
Networks have been recently proposed for modeling dynamics in several kinds of psychological phenomena, such as personality and psychopathology. In this work, we introduce techniques that allow ...disentangling between-subject networks, which encode dynamics that involve stable individual differences, from within-subject networks, which encode dynamics that involve momentary levels of certain individual characteristics. Furthermore, we show how networks can be simultaneously estimated in separate groups of individuals, using a technique called the Fused Graphical Lasso. This technique allows also performing meaningful comparisons among groups. The unique properties of each kind of network are discussed. A tutorial to implement these techniques in the “R” statistical software is presented, together with an example of application.
•We present network-analysis techniques for exploring personality dynamics.•Both between-subject and within-subject networks are discussed.•The Fused Graphical Lasso for estimating networks in different groups is presented.•An example of application is shown in the domain of interpersonal perceptions.•A detailed tutorial in R for implementing these techniques is provided.
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