Analyzing the Structure of U.S. Patents Network

Abstract. The U.S. patents network is a network of almost 3.8 millions patents (network vertices) from the year 1963 to 1999 We analyzed the U.S. patents network with the tools of network analysis in order to get insight into the structure of the network as an initial step to the study of innovations and technical changes based on patents citation network data. In our approach the SPC (Search Path Count) weights, proposed by Hummon and Doreian (1989), for vertices and arcs are calculated first. Based on these weights vertex and line island

Probabilistic Inductive Classes of Graphs

Models of complex networks are generally defined as graph stochastic processes in which edges and vertices are added or deleted over time to simulate the evolution of networks. Here, we define a unifying framework - probabilistic inductive classes of graphs - for formalizing and studying evolution of complex networks. Our definition of probabilistic inductive class of graphs (PICG) extends the standard notion of inductive class of graphs (ICG) by imposing a probability space. A PICG is given by: (1) class B of initial graphs, the basis of PICG, (2) class R of generating rules, each with distinguished left element to which the rule is applied to obtain the right element, (3) probability distribution specifying how the initial graph is chosen from class B, (4) probability distribution specifying how the rules from class R are applied, and, finally, (5) probability distribution specifying how the left elements for every rule in class R are chosen. We point out that many of the existing models of growing networks can be cast as PICGs. We present how the well known model of growing networks - the preferential attachment model - can be studied as PICG. As an illustration we present results regarding the size, order, and degree sequence for PICG models of connected and 2-connected graphs.Comment: 15 pages, 6 figure

Effectiveness and safety of anticoagulant versus antiplatelet therapy in patients after endovascular revascularisation of the lower limb

Background: After revascularisation, patients with peripheral arterial disease (PAD) are routinely prescribed antiplatelet treatment (APT). Patients who receive anticoagulant treatment (ACT) due to comorbidity are an exception. We set out to determine possible differences in the effectiveness and safety between ACT and APT in patients after endovascular revascularisation of the lower limb arteries. Methods: In a single-centre retrospective cohort study, we analysed the data of 1,587 PAD patients who underwent successful endovascular revascularisation of the lower limb arteries due to disabling intermittent claudication or chronic critical limb ischemia over a 5-year period. Patients were divided into the ACT and APT groups based on their prescribed treatment. After balancing both groups’ baseline characteristics with propensity score matching, we compared the effectiveness and safety of both treatment regimens in the first year after revascularisation. Results: Compared to patients with APT, patients with ACT were older, and more often reported arterial hypertension, diabetes, chronic kidney disease, congestive heart failure, ischaemic heart disease, and prior stroke or transient ischaemic attack. After matching, the odds ratio (OR) for an effective outcome with ACT versus APT was 0.78 (95% CI 0.39–1.59; p=0.502), while the OR for a safe outcome with ACT versus APT was 4.12 (95% CI 0.82–20.73; p=0.085). Conclusions: Patients who required ACT were elderly, had more cardiovascular risk factors and had more advanced PAD than patients with APT. After matching, we found no statistically significant difference in the effectiveness and safety of both treatment regimens; however the wide OR confidence intervals warrant further research

VISUAL LEARNING OF OBJECTS AND SCENES

Models of objects or scenes represent data obtained from sets of training images. A database that contains such models serves us for recognition tasks (e.g. recognition of new input images). Principal Component Analysis (PCA) is one of the widely used methods for appearance-based modeling. However, its drawback is that it is not reliable when training sets of images contaminated with non-Gaussain noise are used. This particular noise is present in most of the realistic images (e.g. Unwanted object occlusions, specular reflections, people on the scene). Here we present a more robustPCA based on the traditional PCA. We introduce the least-squares estimation that is used by traditional PCA with the statically more robust M-estimation. We describe the algorithm for minimazing the M-estimation, which is based on the non-linear iterative Newton method. Most of the outliers are detected after the minimization and the influence of all pixels isreduced with respect to their deviation. Experimental results prove that a robust PCA is more reilable on the noisy training data than traditional PCA. They show method's efficiency on the set of scene images with illumination variations. We also succesfully apply the robust method on training sets of panoramic images with varying illumination. This specific model enables surveillance and view mobile robot localisation

Analysis of U.S. patents network: development of patents over time

The NBER network of U.S. patents from 1963 to 1999 (Hall, Jaffe, Tratjenberg 2001, USPTO) is an example of a very large citation network (3774768 vertices and 16522438 arcs). Using islands algorithm (Zaveršnik, Batagelj, 2004) for the Search Path Count (SPC) weights (Hummon and Doreian 1989Batagelj 2003) the most powerful theme in the entire network was determined. From this we selected a group of companies and categories that appeared and split the entire network into subnetworks according to selected companies and technological categories. We study the general trends and features of the subnetworks over the past thirty-seven years. We propose another approach for studying patents\u27 network as a temporal network. Vertices from the same category in the same time slice are shrunk and then the obtained smaller networks over time are studied. By studying development patterns of the network over time we are trying to determine the general trends in the research and development for the selected companies and categories over the past three decades

Internet core and periphery from a marketing communications perspective

In this article, traditional approaches to Internet metrics are upgraded as part of a lengthy study with insights reached by utilising network analysis methods to improve understanding of conventional reach figures. Groups of structurally equivalent sites and browsing probabilities show relations between websitesʼ audiences and provide information missing in reach figures. A synergetic interpretation of both types of information enables more sophisticated marketing communications planning in the Internet. A national network of competing commercial sites is analysed as a case study.V prispevku na osnovi v zadnjih letih opravljenega raziskovalnega dela nadgradimo tradicionalne pristope za merjenje interneta z rezultati metod za analize omrežij. S tem želimo omogočiti smotrnejšo uporabo podatkov o dosegu (angl. reach) v kombinaciji z razumevanjem prekrivanja množic obiskovalcev spletnih mest. Skupine strukturno enakovrednih spletnih mest in izračuni verjetnosti prekrivanja publike za te skupine celovito opišejo doslej manjkajoče informacije. Šele sinergična interpretacija obeh vrst podatkov (dosega in značilnosti omrežja) omogoči učinkovitejše trženjsko komuniciranje na internetu