Abstract Community networks differ from regular networks by their organic growth patterns—there is no central planning body that would decide how the network is built. Instead, the network grows in a bottom-up fashion as more people express interest in participating in the community and connect with their neighbors. People who participate in community networks are usually volunteers with limited free time. Due to these factors, making the management of community networks simpler and easier for all participants is the key component in boosting their growth. Specifics of individual networks often force communities to develop their own sets of tools and best practices which are hard to share and do not interoperate well with others. We propose a new general community network management platform nodewatcher that is built around the core principle of modularity and extensibility, making it suitable for reuse by different community networks. Devices are configured using a platform-independent configuration which nodewatcher can transform into deployable firmware images, eliminating any manual device configuration, reducing errors, and enabling participation of novice maintainers. An embedded monitoring system enables live overview and validation of the whole community network. We show how the system successfully operates in an actual community wireless network, wlan slovenija.
A lot of research has been done into individual wireless mesh network building blocks. Among them are routing @cite_35 @cite_39 @cite_51 , security @cite_31 , and analyses of topologies, performance, mobility @cite_21 @cite_25 @cite_47 @cite_10 @cite_5 . But research into community network management solutions and best practices still remains scarce. This is why most of the related work in this area comes from the individual community networks which have each developed its own solutions, practices and philosophy.
@cite_35 @cite_47 @cite_10 @cite_21 @cite_39 @cite_5 @cite_31 @cite_51 @cite_25
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In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps. Historically, papers have been physically bound to the journal in which they were published; but in the digital age papers are available individually, no longer tied to their respective journals. Hence, papers now can be read and cited based on their own merits, independently of the journal's physical availability, reputation, or impact factor (IF). We compare the strength of the relationship between journals' IFs and the actual citations received by their respective papers from 1902 to 2009. Throughout most of the 20th century, papers' citation rates were increasingly linked to their respective journals' IFs. However, since 1990, the advent of the digital age, the relation between IFs and paper citations has been weakening. This began first in physics, a field that was quick to make the transition into the electronic domain. Furthermore, since 1990 the overall proportion of highly cited papers coming from highly cited journals has been decreasing and, of these highly cited papers, the proportion not coming from highly cited journals has been increasing. Should this pattern continue, it might bring an end to the use of the IF as a way to evaluate the quality of journals, papers, and researchers. © 2012 Wiley Periodicals, Inc. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results.