Influence of Social Networks on Latent Choice of Electric Cars: A Mixed Logit Specification Using Experimental Design Data

Electric cars can potentially make a substantial contribution to the reduction of pollution and noise. The size of this contribution depends on the acceptance of this new technology in the market. This paper reports on the design and results of an elaborate stated choice experiment to investigate the effects of vehicle attributes, contextual and social network attributes on the latent demand for electric cars. The study contributes to the existing literature primarily by explicitly modelling the effects of different elements of social networks on the latent demand for electric cars. Moreover, the number of attributes included in the study design exceeds the typical number of attributes used in previous research, making the model more sensitive to a larger spectrum of variables. Two different mixed logit models are estimated: one with random parameters for vehicle attributes and contextual attributes and fixed effects for the social network attributes; one with random effects for social network attributes and fixed effects for the remaining attributes. Results indicate substantive differences between these two models in terms of the shape of utility curves. Overall, vehicle attributes are most important in the choice of electric cars, followed by social influence attributes. The effects of social network are relatively small.     

Emergence of social networks via direct and indirect reciprocity

Many models of social network formation implicitly assume that network properties are static in steady-state. In contrast, actual social networks are highly dynamic: allegiances and collaborations expire and may or may not be renewed at a later date. Moreover, empirical studies show that human social networks are dynamic at the individual level but static at the global level: individuals’ degree rankings change considerably over time, whereas network-level metrics such as network diameter and clustering coefficient are relatively stable. There have been some attempts to explain these properties of empirical social networks using agent-based models in which agents play social dilemma games with their immediate neighbours, but can also manipulate their network connections to strategic advantage. However, such models cannot straightforwardly account for reciprocal behaviour based on reputation scores (“indirect reciprocity”), which is known to play an important role in many economic interactions. In order to account for indirect reciprocity, we model the network in a bottom-up fashion: the network emerges from the low-level interactions between agents. By so doing we are able to simultaneously account for the effect of both direct reciprocity (e.g. “tit-for-tat”) as well as indirect reciprocity (helping strangers in order to increase one’s reputation). This leads to a strategic equilibrium in the frequencies with which strategies are adopted in the population as a whole, but intermittent cycling over different strategies at the level of individual agents, which in turn gives rise to social networks which are dynamic at the individual level but stable at the network level.     

Flexible Web services integration: a novel personalised social approach

Abstract

Dynamic composition or integration remains one of the key objectives of Web services technology. This paper aims to propose an innovative approach of dynamic Web services composition based on functional and non-functional attributes and individual preferences. In this approach, social networks of Web services are used to maintain interactions between Web services in order to select and compose Web services that are more tightly related to user’s preferences. We use the concept of Web services community in a social network of Web services to reduce considerably their search space. These communities are created by the direct involvement of Web services providers.     

Usefulness of simulating social phenomena: evidence

This paper discusses partial results of an ongoing project focused on analysing the current usefulness and implications of developing research on agent-based social simulation models beyond academic, hobbyist or educational purposes. Design, development and testing phases of such modelling are discussed along with common issues evidence-driven modellers often face whilst collecting, analysing and modelling quantitative and qualitative data into social simulations. It also includes a discussion on the evidence gathered in published literature and structured interviews with researchers that have lead mid- to long-term (3–5 years) projects on social simulation in Europe and the United States. Finally, good-practice recommendations are put forward by presenting a development methodology fully guided by evidence.     

Modelling socio-ecological systems with MAIA: A biogas infrastructure simulation

Similar to other renewable energy technologies, the development of a biogas infrastructure in the Netherlands is going through social, institutional and ecological evolution. To study this complex evolutionary process, we built a comprehensive agent-based model of this infrastructure. We used an agent-based modelling framework called MAIA to build this model with the initial motivation that it facilitates modelling complex institutional structures. The modelling experience however proved that MAIA can also act as an integrated solution to address other major modelling challenges identified in the literature for modelling evolving socio-ecological systems. Building on comprehensive reviews, we reflect on our modelling experience and address four key challenges of modelling evolving socio-ecological systems using agents: (1) design and parameterization of models of agent behaviour and decision-making, (2) system representation in the social and spatial dimension, (3) integration of socio-demographic, ecological, and biophysical models, (4) verification, validation and sensitivity analysis of such ABMs.     

A Discontinuity Model of Technological Change: Catastrophe Theory and Network Structure

Discontinuities as a crucial aspect of economic systems have been discussed both verbally—particularly in institutionality theory—and formally, chiefly using catastrophe theory. Catastrophe theory has, however, been criticized heavily for lacking micro-foundations and has mainly fallen out of use in economics and social sciences. The present paper proposes a simple catastrophe theory model of technological change with network externalities and reevaluates the value of such a model by adding an agent-based micro layer. To this end an agent-based variant of the model is proposed and investigated specifically with regard to the network structure among the agents. While the macro level of the model produces a classical cusp catastrophe—a result that is preserved in the agent-based form—it is found that the behavior of the model changes locally depending on the network structure, especially if networks with features that resemble social networks (low diameter, high clustering, power law distributed node degree) are considered. While the present work investigates merely an aspect out of a large possibility space, it encourages further research using agent-based catastrophe theory models especially of economic aspects to which catastrophe theory has previously successfully been applied; aspects such as technological and institutional change, economic crises, or industry structure.     

A model of friendship networks based on social network analysis

It is important for teachers to understand the characteristics of forming and changing of friendship networks to manage their classes effectively. Many studies have analyzed friendship networks using agent-based models. Most of the models used were designed for capturing basic phenomena in a micro-viewpoint. The purpose of our study is to simulate actual friendship networks. In this paper, we present an agent-based friendship-network model based on our analysis of friendship networks among university students. We focused on Hiramatsu’s model based on a cost-benefit analysis and modified the model with the aim of closing to the actual friendship. With our modified model, the benefit function was based on their cluster structure. The results with the modified model better matched the actual friendship networks. The clique size in the modified model approached that in the actual friendship networks, and the values of several network properties approached those of the actual friendship networks.     

Towards process validation for complex transport models: A sensitivity analysis of a social network-enhanced activity-travel model

Model validation is a significant issue for the modelling of social network-based transportation models because of the many interacting components (the individuals, the environment, and now the network) in the model.In this paper we focus on a sensitivity analysis for such a model, which is part of a larger validation approach known as process validation. This approach investigates both the structure and behaviour of the model, to evaluate whether the model can be used for prediction.The paper draws on a novel set of experiments with an agent-based model which was developed to explore the effects of social networks on activity and travel behaviour. Several versions of the model were created, beginning with a single day model with no interaction, and then adding in multi-day runs with interactions, in order to demonstrate the validation process.The paper argues that testing the model at different levels of complexity increases confidence in the model and makes it easier to locate components or functionality that require improvement. It concludes by suggesting that this approach to sensitivity testing should be adopted for validation of complex transportation models.     

High utility K-anonymization for social network publishing

Privacy and utility are two main desiderata of good sensitive information publishing schemes. For publishing social networks, many existing algorithms rely on \(k\) -anonymity as a criterion to guarantee privacy protection. They reduce the utility loss by first using the degree sequence to model the structural properties of the original social network and then minimizing the changes on the degree sequence caused by the anonymization process. However, the degree sequence-based graph model is simple, and it fails to capture many important graph topological properties. Consequently, the existing anonymization algorithms that rely on this simple graph model to measure utility cannot guarantee generating anonymized social networks of high utility. In this paper, we propose novel utility measurements that are based on more complex community-based graph models. We also design a general \(k\) -anonymization framework, which can be used with various utility measurements to achieve \(k\) -anonymity with small utility loss on given social networks. Finally, we conduct extensive experimental evaluation on real datasets to evaluate the effectiveness of the new utility measurements proposed. The results demonstrate that our scheme achieves significant improvement on the utility of the anonymized social networks compared with the existing anonymization algorithms. The utility losses of many social network statistics of the anonymized social networks generated by our scheme are under 1 % in most cases.     

Agent-based modeling of energy technology adoption: Empirical integration of social,behavioral, economic,and environmental factors

Agent-based modeling (ABM) techniques for studying human-technical systems face two important challenges. First, agent behavioral rules are often ad hoc, making it difficult to assess the implications of these models within the larger theoretical context. Second, the lack of relevant empirical data precludes many models from being appropriately initialized and validated, limiting the value of such models for exploring emergent properties or for policy evaluation. To address these issues, in this paper we present a theoretically-based and empirically-driven agent-based model of technology adoption, with an application to residential solar photovoltaic (PV). Using household-level resolution for demographic, attitudinal, social network, and environmental variables, the integrated ABM framework we develop is applied to real-world data covering 2004–2013 for a residential solar PV program at the city scale. Two applications of the model focusing on rebate program design are also presented.     

Diffusion of hurricane evacuation behavior through a home-workplace social network: A spatially explicit agent-based simulation model

With coastal populations experiencing the growing threat of hurricanes as a consequence of global climate change, models for predicting how household evacuation behavior may diffuse over time and space are needed for emergency management. This study models the effects of social influence on household evacuation behavior in anticipation of a hurricane event. An agent-based model was developed in this study to simulate: 1) a home-workplace social network between households residing in the Florida Keys, 2) the communication of a hurricane evacuation order among socially linked households, and 3) the resulting spatio-temporal diffusion of household evacuation behavior. Data sources informing model implementation include U.S. Census block group data, business databases, and statistics from hurricane evacuation surveys. Simulated model results from the model were validated with empirical traffic records observed at a Florida Keys monitoring station during evacuation from Hurricane Georges in 1998. This model builds upon previous research using agent-based models to simulate hurricane evacuation by incorporating multiple data sources and validating results with empirical traffic patterns. Such an empirically-grounded model facilitates locally relevant exploration of evacuation behavior to support the development of more effective evacuation plans and preparedness for future hurricane events.     

Merging social computing with content: a proposal of a new film platform,Avids

ABSTRACT

Film consumers are continuously online and active in various social platforms. This phenomenon has led over-the-top (OTT) providers – empowered by social computing technologies – to establish a social media presence and incorporate elements drawn from social media into their services. However, little is known about existing OTT interfaces and their key social features. This study sought to provide a structured categorisation of the most salient social media features of the best-known applications in the OTT video business. In addition, a new social content network model, Avids, was proposed to connect individuals socially through films. Avids reaches beyond more fixed, functionality-based approaches applied in the development of OTT video sites and focuses on components related to sociality. This approach ensures a unified system in which the overall social media setting is embedded in every functional area of the platform’s architecture, thereby allowing applications to trigger and support social behaviours absent from traditional OTT providers. A purpose-built international online survey was administered to 479 film lovers to assess how Avids’ main features compare with traditional OTT video providers. The questionnaire was based on the technology acceptance model. The results confirm the critical role of sociality in film viewing-related activities.     

Neurofuzzy identification of an autonomous underwater vehicle

Neurofuzzy modelling is ideally suited to many nonlinear system identification and data modelling applications. By combining the attractive attributes of fuzzy systems and neural networks transparent models of ill-defined systems can be identified. Available expert a priori knowledge is used to construct an initial model. Data modelling techniques from the neural network, statistical and conventional system identification communities are then used to adapt these models. As a result accurate parsimonious models which are transparent and easy to validate are identified. Recent advances in the datadriven identification algorithms have now made neurofuzzy modelling appropriate for high-dimensional problems for which the expert knowledge and data may be of a poor quality. In this paper neurofuzzy modelling techniques are presented. This powerful approach to system identification is demonstrated by its application to the identification of an Autonomous Underwater Vehicle (AUV).     

Incremental K-clique clustering in dynamic social networks

Clustering entities into dense parts is an important issue in social network analysis. Real social networks usually evolve over time and it remains a problem to efficiently cluster dynamic social networks. In this paper, a dynamic social network is modeled as an initial graph with an infinite change stream, called change stream model, which naturally eliminates the parameter setting problem of snapshot graph model. Based on the change stream model, the incremental version of a well known k-clique clustering problem is studied and incremental k-clique clustering algorithms are proposed based on local DFS (depth first search) forest updating technique. It is theoretically proved that the proposed algorithms outperform corresponding static ones and incremental spectral clustering algorithm in terms of time complexity. The practical performances of our algorithms are extensively evaluated and compared with the baseline algorithms on ENRON and DBLP datasets. Experimental results show that incremental k-clique clustering algorithms are much more efficient than corresponding static ones, and have no accumulating errors that incremental spectral clustering algorithm has and can capture the evolving details of the clusters that snapshot graph model based algorithms miss.     

Global and local modelling in RBF networks

Radial basis function networks are traditionally known as local approximation networks as they are composed by a number of elements which, individually, mainly take care of the approximation about a specific area of the input space. Then, the joint global output of the network is obtained as a linear combination of the individual elements' output. However, in the network optimization, the performance of the global model is normally the only objective to optimize. This might cause a deficient local modelling of the input space, thus partially losing the local character of this type of models. This work presents a modified radial basis function network that maintains the approximation capabilities of the local sub-models whereas the model is globally optimized. This property is obtained thanks to a special partitioning of the input space, that leads to a direct global-local optimization. A learning methodology adapted to the proposed model is used in the simulations, consisting of a clustering algorithm for the initialization of the centers and a local search technique. In the experiments, the proposed model shows satisfactory local and global modelling capabilities both in artificial and real applications.     

Online social networking and psychological experiences: The perceptions of young people with mental health difficulties

ObjectivesThis study explores the interaction between online social networking experiences and wellbeing in 12 young people accessing mental health services.MethodsData from semi-structured interviews was analysed using Grounded Theory methodology.Results“Threats and judgement” and “connection and support” were experienced by adolescents, facilitated by having continuous access to a vast social network. These experiences influenced adolescents' psychological wellbeing, mediated by their responses to threat and judgement and maintaining “safe sharing” with their network. Social network use was conceived as a gamble of balancing its potentially positive and negative impact in a culture in which social network use appears to be unavoidable.ConclusionsThe findings indicate the importance of routine assessment and formulation of social networking use in understanding adolescents' psychological distress. Furthermore, a range of opportunities exist for clinicians to utilise the anonymity and peer support that social networks offer to broaden the range of mental health services offered to young people.     

Imputation of missing links and attributes in longitudinal social surveys

The predictive analysis of longitudinal social surveys is highly sensitive to the effects of missing data in temporal observations. Such high sensitivity to missing values raises the need for accurate data imputation, because without it a large fraction of collected data could not be used properly. Previous studies focused on the treatment of missing data in longitudinal social networks due to non-respondents and dealt with the problem largely by imputing missing links in isolation or analyzing the imputation effects on network statistics. We propose to account for changing network topology and interdependence between actors’ links and attributes to construct a unified approach for imputation of links and attributes in longitudinal social surveys. The new method, based on an exponential random graph model, is evaluated experimentally for five scenarios of missing data models utilizing synthetic and real life datasets with 20 %–60 % of nodes missing. The obtained results outperformed all alternatives, four of which were link imputation methods and two node attribute imputation methods. We further discuss the applicability and scalability of our approach to real life problems and compare our model with the latest advancements in the field. Our findings suggest that the proposed method can be used as a viable imputation tool in longitudinal studies.     

Unsupervised social network embedding via adaptive specific mappings

In this paper, we address the problem of unsuperised social network embedding, which aims to embed network nodes, including node attributes, into a latent low dimensional space. In recent methods, the fusion mechanism of node attributes and network structure has been proposed for the problem and achieved impressive prediction performance. However, the non-linear property of node attributes and network structure is not efficiently fused in existing methods, which is potentially helpful in learning a better network embedding. To this end, in this paper, we propose a novel model called ASM (Adaptive Specific Mapping) based on encoder-decoder framework. In encoder, we use the kernel mapping to capture the non-linear property of both node attributes and network structure. In particular, we adopt two feature mapping functions, namely an untrainable function for node attributes and a trainable function for network structure. By the mapping functions, we obtain the low dimensional feature vectors for node attributes and network structure, respectively. Then, we design an attention layer to combine the learning of both feature vectors and adaptively learn the node embedding. In encoder, we adopt the component of reconstruction for the training process of learning node attributes and network structure. We conducted a set of experiments on seven real-world social network datasets. The experimental results verify the effectiveness and efficiency of our method in comparison with state-of-the-art baselines.     

Community detection for emerging social networks

Many famous online social networks, e.g., Facebook and Twitter, have achieved great success in the last several years. Users in these online social networks can establish various connections via both social links and shared attribute information. Discovering groups of users who are strongly connected internally is defined as the community detection problem. Community detection problem is very important for online social networks and has extensive applications in various social services. Meanwhile, besides these popular social networks, a large number of new social networks offering specific services also spring up in recent years. Community detection can be even more important for new networks as high quality community detection results enable new networks to provide better services, which can help attract more users effectively. In this paper, we will study the community detection problem for new networks, which is formally defined as the “New Network Community Detection” problem. New network community detection problem is very challenging to solve for the reason that information in new networks can be too sparse to calculate effective similarity scores among users, which is crucial in community detection. However, we notice that, nowadays, users usually join multiple social networks simultaneously and those who are involved in a new network may have been using other well-developed social networks for a long time. With full considerations of network difference issues, we propose to propagate useful information from other well-established networks to the new network with efficient information propagation models to overcome the shortage of information problem. An effective and efficient method, Cat (Cold stArT community detector), is proposed in this paper to detect communities for new networks using information from multiple heterogeneous social networks simultaneously. Extensive experiments conducted on real-world heterogeneous online social networks demonstrate that Cat can address the new network community detection problem effectively.