Generating realistic scaled complex networks

Research on generative models is a central project in the emerging field of network science, and it studies how statistical patterns found in real networks could be generated by formal rules. Output from these generative models is then the basis for designing and evaluating computational methods on networks, and for verification and simulation studies. During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks. In this study, we (a) introduce a new generator, termed ReCoN; (b) explore how ReCoN and some existing models can be fitted to an original network to produce a structurally similar replica, (c) use ReCoN to produce networks much larger than the original exemplar, and finally (d) discuss open problems and promising research directions. In a comparative experimental study, we find that ReCoN is often superior to many other state-of-the-art network generation methods. We argue that ReCoN is a scalable and effective tool for modeling a given network while preserving important properties at both micro- and macroscopic scales, and for scaling the exemplar data by orders of magnitude in size.Comment: 26 pages, 13 figures, extended version, a preliminary version of the paper was presented at the 5th International Workshop on Complex Networks and their Application

Fast generation of dynamic complex networks with underlying hyperbolic geometry

Complex networks have become increasingly popular for modeling real-world phenomena, ranging from web hyperlinks to interactions between people. Realistic generative network models are important in this context as they avoid privacy concerns of real data and simplify complex network research regarding data sharing, reproducibility, and scalability studies. We study a geometric model creating unitdisk graphs in hyperbolic space. Previous work provided empirical and theoretical evidence that this model creates networks with a hierarchical structure and other realistic features. However, the investigated networks were small, possibly due to a quadratic running time of a straightforward implementation. We provide a faster generator for a representative subset of these networks. Our experiments indicate a time complexity of O((n+m) log n) for our implementation and thus confirm our theoretical considerations. To our knowledge our implementation is the first one with subquadratic running time. The acceleration stems primarily from the reduction of pairwise distance computations through a polar quadtree newly adapted to hyperbolic space. We also extend the generator to an alternative dynamic model which preserves graph properties in expectation. Finally, we generate and evaluate the largest networks of this model published so far. Our parallel implementation computes networks with billions of edges on a shared-memory server in a matter of few minutes. A comprehensive network analysis shows that important features of complex networks, such as a low diameter, power-law degree distribution and a high clustering coefficient, are retained over different graph sizes and densities

Generating realistic scaled complex networks

Research on generative models plays a central role in the emerging field of network science, studying how statistical patterns found in real networks could be generated by formal rules. Output from these generative models is then the basis for designing and evaluating computational methods on networks including verification and simulation studies. During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks. In this study, we (a) introduce a new generator, termed ReCoN; (b) explore how ReCoN and some existing models can be fitted to an original network to produce a structurally similar replica, (c) use ReCoN to produce networks much larger than the original exemplar, and finally (d) discuss open problems and promising research directions. In a comparative experimental study, we find that ReCoN is often superior to many other state-of-the-art network generation methods. We argue that ReCoN is a scalable and effective tool for modeling a given network while preserving important properties at both micro- and macroscopic scales, and for scaling the exemplar data by orders of magnitude in size

Tuning hardness in calcite by incorporation of amino acids

Structural biominerals are inorganic/organic composites that exhibit remarkable mechanical properties. However, the structure–property relationships of even the simplest building unit—mineral single crystals containing embedded macromolecules—remain poorly understood. Here, by means of a model biomineral made from calcite single crystals containing glycine (0–7 mol%) or aspartic acid (0–4 mol%), we elucidate the origin of the superior hardness of biogenic calcite. We analysed lattice distortions in these model crystals by using X-ray diffraction and molecular dynamics simulations, and by means of solid-state nuclear magnetic resonance show that the amino acids are incorporated as individual molecules. We also demonstrate that nanoindentation hardness increased with amino acid content, reaching values equivalent to their biogenic counterparts. A dislocation pinning model reveals that the enhanced hardness is determined by the force required to cut covalent bonds in the molecules

Identification and Characterization of Peripheral T-Cell Lymphoma-Associated SEREX Antigens

Peripheral T-cell lymphomas (PTCL) are generally less common and pursue a more aggressive clinical course than B-cell lymphomas, with the T-cell phenotype itself being a poor prognostic factor in adult non-Hodgkin lymphoma (NHL). With notable exceptions such as ALK+ anaplastic large cell lymphoma (ALCL, ALK+), the molecular abnormalities in PTCL remain poorly characterised. We had previously identified circulating antibodies to ALK in patients with ALCL, ALK+. Thus, as a strategy to identify potential antigens associated with the pathogenesis of PTCL, not otherwise specified (PTCL, NOS), we screened a testis cDNA library with sera from four PTCL, NOS patients using the SEREX (serological analysis of recombinant cDNA expression libraries) technique. We identified nine PTCL, NOS-associated antigens whose immunological reactivity was further investigated using sera from 52 B- and T-cell lymphoma patients and 17 normal controls. The centrosomal protein CEP250 was specifically recognised by patients sera and showed increased protein expression in cell lines derived from T-cell versus B-cell malignancies. TCEB3, BECN1, and two previously uncharacterised proteins, c14orf93 and ZBTB44, were preferentially recognised by patients' sera. Transcripts for all nine genes were identified in 39 cancer cell lines and the five genes encoding preferentially lymphoma-recognised antigens were widely expressed in normal tissues and mononuclear cell subsets. In summary, this study identifies novel molecules that are immunologically recognised in vivo by patients with PTCL, NOS. Future studies are needed to determine whether these tumor antigens play a role in the pathogenesis of PTCL