Social Networks最新文献

Much of the theory of estimation for exponential family models, which include exponential-family random graph models (ERGMs) as a special case, is well-established and maximum likelihood estimates (MLEs) in particular enjoy many desirable properties. However, in the case of many ERGMs, direct calculation of MLEs is impossible and therefore methods for approximating MLEs and/or alternative estimation methods must be employed. Many MLE approximation algorithms require an alternative estimate as a starting point. The maximum pseudo-likelihood estimator (MPLE) is frequently taken as this starting point. Here, we discuss a potentially large class of such alternatives based on the fact that, unlike the MLE, the MPLE fails to satisfy the so-called “likelihood principle”. This means that different networks may have different MPLEs even if they have the same sufficient statistics. We exploit this fact here to search for improved starting values for approximation-based MLE methods. The method we propose has shown its merit in producing an MLE for a network dataset and model that had defied estimation using all other known methods.

Network data uniquely allow –relationships to be multiply reported, creating varying rates of relationship nomination reciprocation. However, what drives such variation is unclear. Variation in reciprocation may reflect substantive information about relationships (e.g., social salience or desirability) or study design (e.g., question wording or capped nominations). We examine predictors of nomination reciprocity in romantic network data from the PROSPER study to analyze individual and dyadic predictors of nomination reciprocity. Results show higher grades predict higher reciprocity, while same-sex relationships and behaviorally discordant dyads are less likely to be reciprocal.

Venture capital (VC) is a relatively newly emergent industry that is still subject to large uncertainties in China. Therefore, building a robust social network with other VC institutions is a good way to share information, various resources, and benefit from skill and knowledge complementarity to against risks. Strong evidences indicate that better networked VC institutions are of a better financial performance, however, most of previous works overlook the evolution of VC institutions and only focus on some simple topology indicators of the static syndication network, which also neglects higher-order network structure and cannot give a comprehensive evaluation. In this paper, based on VC investment records in the Chinese market, we construct temporal syndication networks between VC institutions year by year. As k-shell decomposition considers higher-order connection patterns, we employ k-shell as an evaluation of the influence of VC institutions in syndication networks. By clustering time series of k-shell values, the VC institutions in China fall into five groups that are quite different from each other on financial performances and investment behaviors. This, in turn, proves the power of our method that only based on proper sequential network properties, we can reveal their financial investment performance. Compared to other network centrality measurements, k-shell is a better indicator that is indicated by a smaller intra-group distance and a larger inter-group distance.

The extent to which a policy actor is perceived as being influential by others can shape their role in a policy process. The interest group literature has examined how the use of advocacy tactics, such as lobbying or media campaigns, contributes to an actor’s perceived influence. The policy networks literature, in turn, has found that network ties and occupying certain institutional roles can explain why actors are perceived as influential. When investigating what explains perceptions of influence, interest groups scholars have not accounted for network interdependencies and network scholars have so far not examined the advocacy tactics used by interest groups. This paper addresses the gap at the intersection of these two literatures by investigating the relationship between network ties, institutional roles, advocacy tactics and the presence of influence attribution ties in climate change policy networks. Exponential random graph models are applied to network data collected from the organisations participating in the national climate change policymaking processes in six EU countries that vary by the extent to which they are majoritarian or consensual democracies: Czechia, Finland, Germany, Ireland, Portugal, and Sweden. The results show that network ties and institutional roles are better predictors of influence attribution ties than advocacy tactics and that there is no pattern in the relationship between advocacy tactics and influence attribution ties across different institutional contexts. These findings suggest that because influence is primarily associated with structural factors (network ties and institutional roles) that more established policy actors are likely to have more influence, which may inhibit the need for a significant step change in climate policies.

How do individuals make sense of the specific issues that motivate them when they participate in activism? Are there clear and consistent patterns of motivations among participants and organizers within a movement over time? This paper applies network methods to a unique dataset to understand the relationships and structure formed among the issues that activists cited as motivation for their participation in a series of climate-related demonstrations over a three-year period. We test whether these networks of issue motivations form coherent structures, and whether they reflect the growing emphasis on social justice within the climate movement. Our findings highlight the coherence in the structure of relationships among motivations to participate, especially the centrality of ‘Equality’ as a motivation within our sampled cycle of contention.

We investigate the psycho-linguistic features of the online discourse over climate change, focusing on its modifications throughout the years 2017–2019 as a result of collective actions emerging and spreading worldwide. We seek to understand the emerging connection between digital activism and the psychological processes related to its social drives. To this end, a semantic network is derived from the social platform Twitter, and its evolution is traced over time, tracking textual proxies of social identity and empowerment. Original proposals are made to identify communities and highlight the most important semantic contents of the corpus from a network perspective. These evaluations on semantic communities of related concepts further detail the shift in the rhetoric of collective actions. Finally, we explore projection of the ingroup to the future in the online discourse about climate change, which can point to developments of pro-environmental campaigns.

Extant research on policy networks tends to focus on explaining successes and/or failures of particular policy efforts. One commonly used theoretical framework – the Advocacy Coalition Framework (ACF) – focuses on actor attributes external to policy networks. We argue this leads to an incomplete understanding of the social dynamics of climate change policy making. We incorporate a policy network analytic approach with the ACF in an ERGM of collaboration in a Canadian climate change policy network, showing the role micro-structural network processes play in giving rise to informal policy networks. We find certain policy beliefs are correlated with tie formation. We also find micro-structural network processes related to reciprocity, structural equivalence and transitive closure are correlated with tie formation. We argue combining these two prominent streams of policy network literature has potential to improve our understanding of climate change policy making processes.

As climate change impacts the Great Lakes region, the increased frequency and intensity of precipitation events are dramatically increasing the erosive effects of stormwater in the ravine ecosystem. Because formal governance structures may not be effective in providing coordination during turbulent and dynamic events, those seeking to manage natural resources during climate change may turn to resources located in their social networks. In this study network analysis was used to visualize collegial ties and self-reported consideration of climate change among those managing ravines along southwestern Lake Michigan. Professional development then leveraged the visualizations to modify the network, potentially increasing knowledge flows and coordination. Those who were targeted by the intervention increased their engagement in the network and contributed to bridging between subgroups to fill structural holes. This provides insight into the potential for interventions that leverage baseline network analysis for the management of natural resources in the context of climate change.

Local authorities play a key role in tackling climate change by implementing targeted adaptation and mitigation measures. The specific implementation of a mix of adaptation and mitigation strategies is the outcome of the interaction of policymakers through a political debate and their attitudes towards climate change. By concentrating on the political discourses occurring in the Assembly of an Italian region (Emilia-Romagna), we use a multi-method approach of Discourse Network Analysis and Concept Mapping to investigate local policymakers’ positioning. Our investigation shows that actors are grouped not only according to their political affiliation, but also to the debated topics, and this relates to the preference for supporting adaptation or mitigation measures, which characterizes the local policy debate.