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International environmental treaties are the key means by which states overcome collective action problems and make specific commitments to address environmental issues. However, systematically assessing states' influence in promoting global environmental protection has proven difficult. Analyzing newly compiled data with a purpose-built statistical model, we provide a novel measurement of state influence within the scope of environmental politics and find strong influences among states and treaties. Specifically, we report evidence that states are less likely to ratify when states within their region ratify, and results suggesting that countries positively influence other countries at similar levels of economic development. By examining several prominent treaties, we illustrate the complex nature of influence: a single act of ratification can dramatically reshape global environmental politics. More generally, our findings and approach provide an innovative means to understand the evolution and complexity of international environmental protection.

An avalanche of empirical studies has addressed the validity of the rank-size rule (or Zipf's law) in a multi-city context in many countries. City size in most countries seems to obey Zipf's law, but the question under which conditions (e.g. sample size, spatial scale) this 'law' holds remained largely underinvestigated. Another complementary question is whether socio-economic networks in space also show a similar hierarchical pattern. Against this background, the present paper investigates – from a methodological viewpoint – the relationship between network connectivity and the rank-size rule (or Zipf's law) in an urban-economic network constellation. After a review of the literature, we address in particular the following methodological issues: (i) the (aggregate) behavioural foundation underlying the rank-size rule/Zipf's law in the light of spatial-economic network theories (e.g. entropy maximization, spatial interaction theory, etc.); (ii) the nature of the analytical relationship between social-spatial network analysis and the rank-size rule/Zipf's law. We argue that the rank size rule is compatible with conventional economic foundations of spatial network models. Consequently, a spatial-economic interpretation – as well as a network connectivity interpretation – of the rank-size rule coefficient is provided. Our methodological contribution forms the foundation for the subsequent empirical analysis applied to spatial networks in a socio-economic context. The aim here is to test the sensitivity of empirical findings for changes in scale, functional forms, time periods, and network structures. Our application is concerned with an extensive spatio-temporal panel database related to the evolution of urban population in Germany. We test the relevance of the rank-size rule/Zipf's law, and its evolution over the years, and – in parallel – the related 'socio-economic' connectivity in these urban networks. In particular, we will show that Zipf's law (i.e., with the rank-size coefficient equal to 1) is only valid under particular conditions of the sample size. The paper concludes with some retrospective and prospective remarks.

Abstract
Complex networks describe a wide range of systems in nature and society. As a fundamental concept of graph theory, the path connecting nodes and edges plays a vital role in network science. Rather than focusing on the path length or path centrality, here we draw attention to the path multiplicity related to decision-making efficiency, which is defined as the number of shortest paths between node pairs and thus characterizes the routing choice diversity. Notably, through extensive empirical investigations from this new perspective, we surprisingly observe a "hesitant-world" feature along with the "small-world" feature, and find a universal power-law of the path multiplicity, meaning that a small number of node pairs possess high path multiplicity. We demonstrate that the power-law of path multiplicity is much stronger than the power-law of node degree, which is known as the scale-free property. Then we show that these phenomena can not be captured by existing classical network models. Furthermore, we explore the relationship between the path multiplicity and existing typical network metrics, such as average shortest path length, clustering coefficient, assortativity coefficient and node centralities. We demonstrate that the path multiplicity is a distinctive network metric. These results expand our knowledge of network structure and provide a novel viewpoint for network design and optimization with significant potential applications in biological, social and man-made networks.

In our globally connected and online world today, massive amounts of data flow across countless networks. Much of that data is ephemeral, meaning that after it has passed across the network, it's gone. For law enforcement and IT professionals who are engaged in digital forensic investigations, understanding how to read the data transmitted on a network is crucial to carrying out their task. This is especially important in the case of incident response and corporate investigations and can help in identifying unauthorized intrusions on a network. If want to become part of the ever-growing ranks of digital investigators and you have entry- to intermediate-level computer skills, this course will show you how to use the industry-favorite tool Wireshark to capture and analyze network traffic. You'll learn how to extract files from the "packets" that make up digital transmissions over a network. And, you'll see how to locate and identify the important header information and metadata contained in those packets.

This article has two aims. First, we develop a dialectical model of the role that policy networks play in any explanation of policy outcomes. Our model is based upon a critique of existing approaches and emphasizes that the relationship between networks and outcomes is not a simple, unidimensional one. Rather, we argue that there are three interactive or dialectical relationships involved between: the structure of the network and the agents operating within them; the network and the context within which it operates; and the network and the policy outcome. Second, we use this model to help analyse and understand continuity and change in British agricultural policy since the 1930s. Obviously, one case is not sufficient to establish the utility of the model, but the case does illustrate both that policy networks can, and do, affect policy outcomes and that, in order to understand how that happens, we need to appreciate the role played by the three dialectical relationships highlighted in our model.

This paper considers a vertical differentiation model with positive network effects. It is based on the assumption that consumers belong to the same network if they buy products exhibiting close characteristics. Thus, the network has two characteristics: its intensity and its selectivity. The higher the network intensity, the more positively affected the consumer is by the network size. The network selectivity corresponds to how close the products' characteristics should be to be perceived by consumers as compatible. The closer two compatible products are in the product space, the more selective the network.We analyze the strategic behavior in terms of prices and qualities of two firms: an incumbent and a potential entrant. By choosing its quality the entrant chooses at the same time to be compatible or not.We show that if the network intensity is moderate, it is always profitable for the entrant to enter the market. In this case the entrant is compatible with the incumbent when the network is not very selective and is incompatible when the network is selective. If the network intensity is strong, we prove that it acts as a barrier to entry under incompatibility. The equilibrium outcomes in this case are either no entry or compatible.

Social networks constitute an intrinsic part of our lives, not only in private aspects, but also professional and academic ones. Digitalisation in various professional and scientific fields, web 2.0, universal mobility and the growing use of multimedia tools in the classroom have led to the use of social networks as teaching material and tools.
In this work, the main social networks suitable for language teaching will be illustrated and various possibilities for their use will be indicated to derive maximum benefit from them in the classroom or during the student's independent study. Activities that may include the use of digital applications and the ways in which these platforms can be inserted into the different phases of the lesson will be indicated.
Finally, various practical examples of the inclusion of social networks in the classroom will be given, accompanied by intentional reflections to encourage language teachers to include web 2.0 tools among their working methods.