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This article empirically tests the role of legacy and digital-born news media, mapping the patterns of audience navigation across news sources and the relationship between news providers. We borrow tools from network science to bring evidence that suggest legacy news media retain control of the most central positions in the online news domain. Great progress has been made in discussing theoretically the impact of the Internet on the news media ecology. Less research attention, however, has been given to empirically testing changes in the role of legacy media and the rising prominence of digital-born outlets. To fill this gap, in this study we use the hyperlink-induced topic search algorithm, which identifies authorities by means of a hyperlink network, to show that legacy media are still the most authoritative sources in the media ecology. To further substantiate their dominant role, we also examine the structural position of news providers in the audience network. We gather navigation data from a panel of 30,000 people and use it to reproduce the network of patterns of news consumption. While legacy news media retain control of the brokerage positions for the general population, our analysis--focused on patterns of young news consumers--reveals that new digital outlets also occupy relevant positions to control the audience flow. The results of this study have substantive implications for our understanding of news organizations' roles and how they attain authority in the digital age. [ABSTRACT FROM AUTHOR]

We uncover the global organization of clustering in real complex networks. To this end, we ask whether triangles in real networks organize as in maximally random graphs with given degree and clustering distributions, or as in maximally ordered graph models where triangles are forced into modules. The answer comes by way of exploring m-core landscapes, where the m-core is defined, akin to the k-core, as the maximal subgraph with edges participating in at least m triangles. This property defines a set of nested subgraphs that, contrarily to k-cores, is able to distinguish between hierarchical and modular architectures. We find that the clustering organization in real networks is neither completely random nor ordered although, surprisingly, it is more random than modular. This supports the idea that the structure of real networks may in fact be the outcome of self-organized processes based on local optimization rules, in contrast to global optimization principles. [ABSTRACT FROM AUTHOR]

Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks-the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain-and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs. [ABSTRACT FROM AUTHOR]

It is commonly assumed in percolation theories that at most one percolating cluster can exist in a network. We show that several coexisting percolating clusters (CPCs) can emerge in networks due to limited mixing, i.e., a finite and sufficiently small number of interlinks between network modules. We develop an approach called modular message passing (MMP) to describe and verify these observations. We demonstrate that the appearance of CPCs is an important source of inaccuracy in previously introduced percolation theories, such as the message passing (MP) approach, which is a state-of-the-art theory based on the belief propagation method. Moreover, we show that the MMP theory improves significantly over the predictions of MP for percolation on synthetic networks with limited mixing and also on several real-world networks. These findings have important implications for understanding the robustness of networks and in quantifying epidemic outbreaks in the susceptible-infected-recovered (SIR) model of disease spread. [ABSTRACT FROM AUTHOR]