Assortativity and leadership emerge from anti-preferential attachment in heterogeneous networks

Synchronization in starlike networks of phase oscillators

We fully describe the mechanisms underlying synchronization in starlike networks of phase oscillators. In particular, the routes to synchronization and the critical points for the associated phase transitions are determined analytically. In contrast to the classical Kuramoto theory, we unveil that relaxation rates to each equilibrium state indeed exist and remain invariant under three levels of descriptions corresponding to different geometric implications. The special symmetry in the coupling determines a quasi-Hamiltonian property, which is further unveiled on the basis of singular perturbation theory. Since starlike coupling configurations constitute the building blocks of technological and biological real world networks, our paper paves the way towards the understanding of the functioning of such real world systems in many practical situations.

Mechanisms of complex network growth: Synthesis of the preferential attachment and fitness models

We analyze growth mechanisms of complex networks and focus on their validation by measurements. To this end we consider the equation ΔK=A(t)(K+K_{0})Δt, where K is the node's degree, ΔK is its increment, A(t) is the aging constant, and K_{0} is the initial attractivity. This equation has been commonly used to validate the preferential attachment mechanism. We show that this equation is undiscriminating and holds for the fitness model [Caldarelli et al., Phys. Rev. Lett. 89, 258702 (2002)PRLTAO0031-900710.1103/PhysRevLett.89.258702] as well. In other words, accepted method of the validation of the microscopic mechanism of network growth does not discriminate between "rich-gets-richer" and "good-gets-richer" scenarios. This means that the growth mechanism of many natural complex networks can be based on the fitness model rather than on the preferential attachment, as it was believed so far. The fitness model yields the long-sought explanation for the initial attractivity K_{0}, an elusive parameter which was left unexplained within the framework of the preferential attachment model. We show that the initial attractivity is determined by the width of the fitness distribution. We also present the network growth model based on recursive search with memory and show that this model contains both the preferential attachment and the fitness models as extreme cases.

Author-paper affiliation network architecture influences the methodological quality of systematic reviews and meta-analyses of psoriasis

Moderate-to-severe psoriasis is associated with significant comorbidity, an impaired quality of life, and increased medical costs, including those associated with treatments. Systematic reviews (SRs) and meta-analyses (MAs) of randomized clinical trials are considered two of the best approaches to the summarization of high-quality evidence. However, methodological bias can reduce the validity of conclusions from these types of studies and subsequently impair the quality of decision making. As co-authorship is among the most well-documented forms of research collaboration, the present study aimed to explore whether authors’ collaboration methods might influence the methodological quality of SRs and MAs of psoriasis. Methodological quality was assessed by two raters who extracted information from full articles. After calculating total and per-item Assessment of Multiple Systematic Reviews (AMSTAR) scores, reviews were classified as low (0-4), medium (5-8), or high (9-11) quality. Article metadata and journal-related bibliometric indices were also obtained. A total of 741 authors from 520 different institutions and 32 countries published 220 reviews that were classified as high (17.2%), moderate (55%), or low (27.7%) methodological quality. The high methodological quality subnetwork was larger but had a lower connection density than the low and moderate methodological quality subnetworks; specifically, the former contained relatively fewer nodes (authors and reviews), reviews by authors, and collaborators per author. Furthermore, the high methodological quality subnetwork was highly compartmentalized, with several modules representing few poorly interconnected communities. In conclusion, structural differences in author-paper affiliation network may influence the methodological quality of SRs and MAs on psoriasis. As the author-paper affiliation network structure affects study quality in this research field, authors who maintain an appropriate balance between scientific quality and productivity are more likely to develop higher quality reviews.

Social network analysis of the biblical Moses

Here, social network analysis approaches are used to characterize the figure of the biblical Moses, and his relationship with characters from the books of the Pentateuch; Genesis, Exodus, Leviticus, Numbers and Deuteronomy. The potential value of using such quantitative approaches is explored in relation to other forms of textual exegesis. Using a maximum likelihood approach, the degree distributions of the social networks are shown to approximate to a power law with exponential cutoff. The node representing Moses is very highly connected and falls outside the best fit line, as does the node representing Yahweh, which may indicate authorial emphasis. Only the social network from Genesis is assortative, a property typical of many real world social networks. A substantial proportion of disassortativity in the social network based around Moses disappears when the node is removed, potentially indicating some artificiality in its orientation within the network. The approximation of the degree distributions to a power law with exponential cutoff represents an emergent property resulting from the combinatorial and collaborative manner of composition, and indicates a bounding constraint on more highly connected nodes. Unusually highly connected nodes representing the deity and prophet may be characteristic of social networks derived from religious texts.

Localized recovery of complex networks against failure

Resilience of complex networks to failure has been an important issue in network research for decades, and recent studies have begun to focus on the inverse recovery of network functionality through strategically healing missing nodes or edges. However, the effect of network recovery is far from fully understood, and a general theory is still missing. Here we propose and study a general model of localized recovery, where a group of neighboring nodes are restored in an invasive way from a seed node. We develop a theoretical framework to compare the effect of random recovery (RR) and localized recovery (LR) in complex networks including Erdős-Rényi networks, random regular networks, and scale-free networks. We find detailed phase diagrams for the subnetwork of occupied nodes and the "complement network" of failed nodes under RR and LR. By identifying the two competitive forces behind LR, we present an analytical and numerical approach to guide us in choosing the appropriate recovery strategy and provide estimation on its effect by using the degree distribution of the original network as the only input. Our work therefore provides insight for quantitatively understanding recovery process and its implications in infrastructure protection in various complex systems.