Formal vs self-organised knowledge systems: a network approach

In this work we consider the topological analysis of symbolic formal systems in the framework of network theory. In particular we analyse the network extracted by Principia Mathematica of B. Russell and A.N. Whitehead, where the vertices are the statements and two statements are connected with a directed link if one statement is used to demonstrate the other one. We compare the obtained network with other directed acyclic graphs, such as a scientific citation network and a stochastic model. We also introduce a novel topological ordering for directed acyclic graphs and we discuss its properties in respect to the classical one. The main result is the observation that formal systems of knowledge topologically behave similarly to self-organised systems.Comment: research pape

Situating graphs as workplace knowledge

We investigate the use and knowledge of graphs in the context of a large industrial factory. We are particularly interested in the question of "transparency", a question that has been extensively considered in the general literature on tool use, and more recently, by Michael Roth and his colleagues in the context of scientific work. Roth uses the notion of transparency to characterise instances of graph use by highly educated scientists in cases where the context was familiar: the scientists were able to read the situation "through" the graph. This paper explores the limits of the validity of the transparency metaphor. We present two vignettes of actual graph use by a factory worker, and contrast his actions and knowledge with that of a highly-qualified process engineer working on the same production line. We note that in neither case were the graphs transparent. We argue that a fuller account that describes a spectrum of transparency is needed, and we seek to achieve this by adopting some elements of a semiotic approach that enhance a strictly activity-theoretical view

Using a Knowledge Graph to Discover Earth Science Information

Knowledge graphs link key entities within a specific domain to other entities via relationships. Researchers are able to mine these relationships from numerous sources to infer new knowledge. Text extraction from peer-reviewed papers and scientific reports are untapped resources that can be leveraged by knowledge graphs to accelerate scientific discovery

GPU accelerated maximum cardinality matching algorithms for bipartite graphs

We design, implement, and evaluate GPU-based algorithms for the maximum cardinality matching problem in bipartite graphs. Such algorithms have a variety of applications in computer science, scientific computing, bioinformatics, and other areas. To the best of our knowledge, ours is the first study which focuses on GPU implementation of the maximum cardinality matching algorithms. We compare the proposed algorithms with serial and multicore implementations from the literature on a large set of real-life problems where in majority of the cases one of our GPU-accelerated algorithms is demonstrated to be faster than both the sequential and multicore implementations.Comment: 14 pages, 5 figure

Graphs in machine learning: an introduction

Graphs are commonly used to characterise interactions between objects of interest. Because they are based on a straightforward formalism, they are used in many scientific fields from computer science to historical sciences. In this paper, we give an introduction to some methods relying on graphs for learning. This includes both unsupervised and supervised methods. Unsupervised learning algorithms usually aim at visualising graphs in latent spaces and/or clustering the nodes. Both focus on extracting knowledge from graph topologies. While most existing techniques are only applicable to static graphs, where edges do not evolve through time, recent developments have shown that they could be extended to deal with evolving networks. In a supervised context, one generally aims at inferring labels or numerical values attached to nodes using both the graph and, when they are available, node characteristics. Balancing the two sources of information can be challenging, especially as they can disagree locally or globally. In both contexts, supervised and un-supervised, data can be relational (augmented with one or several global graphs) as described above, or graph valued. In this latter case, each object of interest is given as a full graph (possibly completed by other characteristics). In this context, natural tasks include graph clustering (as in producing clusters of graphs rather than clusters of nodes in a single graph), graph classification, etc. 1 Real networks One of the first practical studies on graphs can be dated back to the original work of Moreno [51] in the 30s. Since then, there has been a growing interest in graph analysis associated with strong developments in the modelling and the processing of these data. Graphs are now used in many scientific fields. In Biology [54, 2, 7], for instance, metabolic networks can describe pathways of biochemical reactions [41], while in social sciences networks are used to represent relation ties between actors [66, 56, 36, 34]. Other examples include powergrids [71] and the web [75]. Recently, networks have also been considered in other areas such as geography [22] and history [59, 39]. In machine learning, networks are seen as powerful tools to model problems in order to extract information from data and for prediction purposes. This is the object of this paper. For more complete surveys, we refer to [28, 62, 49, 45]. In this section, we introduce notations and highlight properties shared by most real networks. In Section 2, we then consider methods aiming at extracting information from a unique network. We will particularly focus on clustering methods where the goal is to find clusters of vertices. Finally, in Section 3, techniques that take a series of networks into account, where each network i

Integrated graphical framework accounting for the nature and the speed of the learning process: an application to MNEs strategies of internationalisation of production and R&D investment

Existing illustrations of the learning phenomenon either stress the relationship between flows and stocks, neglecting the chronological time variable, or the speed of knowledge accumulation along time, neglecting the nature of the underlying learning process. In this paper we present a graphical depiction stressing, in an explicit way, both the nature of interplay between flows and stocks and the intensity of the learning process. The four-quadrant graphs that we develop overcome considerable simplification in literature by deriving, by construction, a measure of dynamic gains of knowledge following the interplay of stock of scientific and technological knowledge and the flow of effort in R&D. This scheme is then applied to study the internationalisation of production and R&D, which are strategies followed by multinational firms. Two types of innovation – process innovation and product innovation – are therefore studied constructing, in each case, an industry performance measure adequately indexed to the cumulated knowledge stock at a given moment in time. In any case, the dynamic efficiency measure adopted naturally takes into account both the absolute changes in the technology indexes and the time delays to reach them, which are properly discounted. Regarding multinationals strategies - internationalisation of production and R&D investment -, we begin with the question of finding a new location for using a now well developed production technology, and then deal with the problem of selecting a region of excellence in research to take gains of concentration advantages and local externalities.Learning; knowledge; technology; R&D; MNEs

Análise de redes de colaboração científica entre educação especial e fonoaudiologia

This article analyses the scientific collaboration networks between Special Education and Speech Therapy. The corpus of the study consisted of 267 articles published by 44 researchers who conducted postgraduate studies in Special Education at the Federal University of São Carlos, between the years 1981 to 2009, whose dissertations and theses characterize the interface between these two areas of knowledge. The data source was Lattes Curriculum. The methodology adopted was the social network analysis (SNA) that allowed constructing scientific collaboration networks through authoring and co-authoring relationships among actors participating in these knowledge areas. The softwares Ucinet and Netdraw were used to map networks and produce graphs to view the collaborations between actors. The results showed the existence of small clusters with few actors who hold the domain for publication; the formation of collaborative networks between advisor and student publications in co-authoring and the collaboration with researchers in the country and abroad. The study also showed that the analysis of scientific collaboration networks in the field of Special Education and Speech Therapy contribute to the development of future research on this interface