Portraits of Complex Networks

We propose a method for characterizing large complex networks by introducing a new matrix structure, unique for a given network, which encodes structural information; provides useful visualization, even for very large networks; and allows for rigorous statistical comparison between networks. Dynamic processes such as percolation can be visualized using animations. Applications to graph theory are discussed, as are generalizations to weighted networks, real-world network similarity testing, and applicability to the graph isomorphism problem.Comment: 6 pages, 9 figure

Spin measurements for 147Sm+n resonances: Further evidence for non-statistical effects

We have determined the spins J of resonances in the 147Sm(n,gamma) reaction by measuring multiplicities of gamma-ray cascades following neutron capture. Using this technique, we were able to determine J values for all but 14 of the 140 known resonances below En = 1 keV, including 41 firm J assignments for resonances whose spins previously were either unknown or tentative. These new spin assignments, together with previously determined resonance parameters, allowed us to extract separate level spacings and neutron strength functions for J = 3 and 4 resonances. Furthermore, several statistical test of the data indicate that very few resonances of either spin have been missed below En = 700eV. Because a non-statistical effect recently was reported near En = 350 eV from an analysis of 147Sm(n,alpha) data, we divided the data into two regions; 0 < En < 350 eV and 350 < En < 700 eV. Using neutron widths from a previous measurement and published techniques for correcting for missed resonances and for testing whether data are consistent with a Porter-Thomas distribution, we found that the reduced-neutron-width distribution for resonances below 350 eV is consistent with the expected Porter-Thomas distribution. On the other hand, we found that reduced-neutron-width data in the 350 < En < 700 eV region are inconsistent with a Porter-Thomas distribution, but in good agreement with a chi-squared distribution having two or more degrees of freedom. We discuss possible explanations for these observed non-statistical effects and their possible relation to similar effects previously observed in other nuclides.Comment: 40 pages, 13 figures, accepted by Phys. Rev.

Twitter-based analysis of the dynamics of collective attention to political parties

Large-scale data from social media have a significant potential to describe complex phenomena in real world and to anticipate collective behaviors such as information spreading and social trends. One specific case of study is represented by the collective attention to the action of political parties. Not surprisingly, researchers and stakeholders tried to correlate parties' presence on social media with their performances in elections. Despite the many efforts, results are still inconclusive since this kind of data is often very noisy and significant signals could be covered by (largely unknown) statistical fluctuations. In this paper we consider the number of tweets (tweet volume) of a party as a proxy of collective attention to the party, identify the dynamics of the volume, and show that this quantity has some information on the elections outcome. We find that the distribution of the tweet volume for each party follows a log-normal distribution with a positive autocorrelation of the volume over short terms, which indicates the volume has large fluctuations of the log-normal distribution yet with a short-term tendency. Furthermore, by measuring the ratio of two consecutive daily tweet volumes, we find that the evolution of the daily volume of a party can be described by means of a geometric Brownian motion (i.e., the logarithm of the volume moves randomly with a trend). Finally, we determine the optimal period of averaging tweet volume for reducing fluctuations and extracting short-term tendencies. We conclude that the tweet volume is a good indicator of parties' success in the elections when considered over an optimal time window. Our study identifies the statistical nature of collective attention to political issues and sheds light on how to model the dynamics of collective attention in social media.Comment: 16 pages, 7 figures, 3 tables. Published in PLoS ON

A meta-analysis of state-of-the-art electoral prediction from Twitter data

Electoral prediction from Twitter data is an appealing research topic. It seems relatively straightforward and the prevailing view is overly optimistic. This is problematic because while simple approaches are assumed to be good enough, core problems are not addressed. Thus, this paper aims to (1) provide a balanced and critical review of the state of the art; (2) cast light on the presume predictive power of Twitter data; and (3) depict a roadmap to push forward the field. Hence, a scheme to characterize Twitter prediction methods is proposed. It covers every aspect from data collection to performance evaluation, through data processing and vote inference. Using that scheme, prior research is analyzed and organized to explain the main approaches taken up to date but also their weaknesses. This is the first meta-analysis of the whole body of research regarding electoral prediction from Twitter data. It reveals that its presumed predictive power regarding electoral prediction has been rather exaggerated: although social media may provide a glimpse on electoral outcomes current research does not provide strong evidence to support it can replace traditional polls. Finally, future lines of research along with a set of requirements they must fulfill are provided.Comment: 19 pages, 3 table

Partisan Asymmetries in Online Political Activity

We examine partisan differences in the behavior, communication patterns and social interactions of more than 18,000 politically-active Twitter users to produce evidence that points to changing levels of partisan engagement with the American online political landscape. Analysis of a network defined by the communication activity of these users in proximity to the 2010 midterm congressional elections reveals a highly segregated, well clustered partisan community structure. Using cluster membership as a high-fidelity (87% accuracy) proxy for political affiliation, we characterize a wide range of differences in the behavior, communication and social connectivity of left- and right-leaning Twitter users. We find that in contrast to the online political dynamics of the 2008 campaign, right-leaning Twitter users exhibit greater levels of political activity, a more tightly interconnected social structure, and a communication network topology that facilitates the rapid and broad dissemination of political information.Comment: 17 pages, 10 figures, 6 table

The Insulin-Like Growth Factor System in the Long-Lived Naked Mole-Rat.

Naked mole-rats (Heterocephalus glaber) (NMRs) are the longest living rodents known. They show negligible senescence, and are resistant to cancers and certain damaging effects associated with aging. The insulin-like growth factors (IGFs) have pluripotent actions, influencing growth processes in virtually every system of the body. They are established contributors to the aging process, confirmed by the demonstration that decreased IGF signaling results in life-extending effects in a variety of species. The IGFs are likewise involved in progression of cancers by mediating survival signals in malignant cells. This report presents a full characterization of the IGF system in the NMR: ligands, receptors, IGF binding proteins (IGFBPs), and IGFBP proteases. A particular emphasis was placed on the IGFBP protease, pregnancy-associated plasma protein-A (PAPP-A), shown to be an important lifespan modulator in mice. Comparisons of IGF-related genes in the NMR with human and murine sequences indicated no major differences in essential parts of the IGF system, including PAPP-A. The protease was shown to possess an intact active site despite the report of a contradictory genome sequence. Furthermore, PAPP-A was expressed and translated in NMRs cells and retained IGF-dependent proteolytic activity towards IGFBP-4 and IGF-independent activity towards IGFBP-5. However, experimental data suggest differential regulatory mechanisms for PAPP-A expression in NMRs than those described in humans and mice. This overall description of the IGF system in the NMR represents an initial step towards elucidating the complex molecular mechanisms underlying longevity, and how these animals have evolved to ensure a delayed and healthy aging process

Structural and Electronic Properties of Small Neutral (MgO)n Clusters

Ab initio Perturbed Ion (PI) calculations are reported for neutral stoichiometric (MgO)n clusters (n<14). An extensive number of isomer structures was identified and studied. For the isomers of (MgO)n (n<8) clusters, a full geometrical relaxation was considered. Correlation corrections were included for all cluster sizes using the Coulomb-Hartree-Fock (CHF) model proposed by Clementi. The results obtained compare favorably to the experimental data and other previous theoretical studies. Inclusion of correlaiotn is crucial in order to achieve a good description of these systems. We find an important number of new isomers which allows us to interpret the experimental magic numbers without the assumption of structures based on (MgO)3 subunits. Finally, as an electronic property, the variations in the cluster ionization potential with the cluster size were studied and related to the structural isomer properties.Comment: 24 pages, LaTeX, 7 figures in GIF format. Accepted for publication in Phys. Rev.

Computational fact checking from knowledge networks

Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation