Distances in random graphs with finite variance degrees

In this paper we study a random graph with $N$ nodes, where node $j$ has degree $D_j$ and $\{D_j\}_{j=1}^N$ are i.i.d. with \prob(D_j\leq x)=F(x). We assume that $1-F(x)\leq c x^{-\tau+1}$ for some $\tau>3$ and some constant $c>0$. This graph model is a variant of the so-called configuration model, and includes heavy tail degrees with finite variance. The minimal number of edges between two arbitrary connected nodes, also known as the graph distance or the hopcount, is investigated when $N\to \infty$. We prove that the graph distance grows like $\log_{\nu}N$, when the base of the logarithm equals \nu=\expec[D_j(D_j -1)]/\expec[D_j]>1. This confirms the heuristic argument of Newman, Strogatz and Watts \cite{NSW00}. In addition, the random fluctuations around this asymptotic mean $\log_{\nu}{N}$ are characterized and shown to be uniformly bounded. In particular, we show convergence in distribution of the centered graph distance along exponentially growing subsequences.Comment: 40 pages, 2 figure

Making Sense:Empowering participatory sensing with transformation design

This paper demonstrates the value of transformation design in participatory sensing anddescribeshow design can inform awareness and develop actions for change to tackle environmental issues. Recent researchadvocatesfor participatory sensing (open data capture through digital platforms) using technology that can assist and inspire citizens in driving environmental change. This paper examines a study aimed at overcoming some of the challenges associated with the sustainability and impact of environmental participatory sensing. Our approachmergesthe fields of participatory sensing and design, and exploreshow transformation design can add an important dynamic in the framing of participatory sensing. It conceptualizes the way thatcommunities increase awareness of environmental issues and take actionto effect positive change. We present a study conducted across three European cities with citizens who were concerned about environmental challenges. Our contribution describes an approach and range of methods for supporting action and chang

Router-level community structure of the Internet Autonomous Systems

The Internet is composed of routing devices connected between them and organized into independent administrative entities: the Autonomous Systems. The existence of different types of Autonomous Systems (like large connectivity providers, Internet Service Providers or universities) together with geographical and economical constraints, turns the Internet into a complex modular and hierarchical network. This organization is reflected in many properties of the Internet topology, like its high degree of clustering and its robustness. In this work, we study the modular structure of the Internet router-level graph in order to assess to what extent the Autonomous Systems satisfy some of the known notions of community structure. We show that the modular structure of the Internet is much richer than what can be captured by the current community detection methods, which are severely affected by resolution limits and by the heterogeneity of the Autonomous Systems. Here we overcome this issue by using a multiresolution detection algorithm combined with a small sample of nodes. We also discuss recent work on community structure in the light of our results

IHMCIF: An Extension of the PDBx/mmCIF Data Standard for Integrative Structure Determination Methods

IHMCIF (github.com/ihmwg/IHMCIF) is a data information framework that supports archiving and disseminating macromolecular structures determined by integrative or hybrid modeling (IHM), and making them Findable, Accessible, Interoperable, and Reusable (FAIR). IHMCIF is an extension of the Protein Data Bank Exchange/macromolecular Crystallographic Information Framework (PDBx/mmCIF) that serves as the framework for the Protein Data Bank (PDB) to archive experimentally determined atomic structures of biological macromolecules and their complexes with one another and small molecule ligands (e.g., enzyme cofactors and drugs). IHMCIF serves as the foundational data standard for the PDB-Dev prototype system, developed for archiving and disseminating integrative structures. It utilizes a flexible data representation to describe integrative structures that span multiple spatiotemporal scales and structural states with definitions for restraints from a variety of experimental methods contributing to integrative structural biology. The IHMCIF extension was created with the benefit of considerable community input and recommendations gathered by the Worldwide Protein Data Bank (wwPDB) Task Force for Integrative or Hybrid Methods (wwpdb.org/task/hybrid). Herein, we describe the development of IHMCIF to support evolving methodologies and ongoing advancements in integrative structural biology. Ultimately, IHMCIF will facilitate the unification of PDB-Dev data and tools with the PDB archive so that integrative structures can be archived and disseminated through PDB

On Characterizing Network Hierarchy

Our previous work in topology characterization and hierarchy [1] introduced a hierarchy metric to explore the hierarchical structure in various networks. This metric is non-intuitive and complicated. In this paper, we propose a simpler and more natural metric for measuring network hierarchy. This simpler metric uses slightly different criteria in selecting backbone links than the more complicated one. Nevertheless, the network classifications according to both metrics agree with each other. Furthermore, we have extended the hierarchy analysis to examine path characteristics and found that the hierarchical nature of degree-based networks better resembles the hierarchy of the Internet at the AS level than at the routerlevel