How Realistic Should Knowledge Diffusion Models Be?

Knowledge diffusion models typically involve two main features: an underlying social network topology on one side, and a particular design of interaction rules driving knowledge transmission on the other side. Acknowledging the need for realistic topologies and adoption behaviors backed by empirical measurements, it becomes unclear how accurately existing models render real-world phenomena: if indeed both topology and transmission mechanisms have a key impact on these phenomena, to which extent does the use of more or less stylized assumptions affect modeling results? In order to evaluate various classical topologies and mechanisms, we push the comparison to more empirical benchmarks: real-world network structures and empirically measured mechanisms. Special attention is paid to appraising the discrepancy between diffusion phenomena (i) on some real network topologies vs. various kinds of scale-free networks, and (ii) using an empirically-measured transmission mechanism, compared with canonical appropriate models such as threshold models. We find very sensible differences between the more realistic settings and their traditional stylized counterparts. On the whole, our point is thus also epistemological by insisting that models should be tested against simulation-based empirical benchmarks.Agent-Based Simulation, Complex Systems, Empirical Calibration and Validation, Knowledge Diffusion, Model Comparison, Social Networks

Precursors and Laggards: An Analysis of Semantic Temporal Relationships on a Blog Network

We explore the hypothesis that it is possible to obtain information about the dynamics of a blog network by analysing the temporal relationships between blogs at a semantic level, and that this type of analysis adds to the knowledge that can be extracted by studying the network only at the structural level of URL links. We present an algorithm to automatically detect fine-grained discussion topics, characterized by n-grams and time intervals. We then propose a probabilistic model to estimate the temporal relationships that blogs have with one another. We define the precursor score of blog A in relation to blog B as the probability that A enters a new topic before B, discounting the effect created by asymmetric posting rates. Network-level metrics of precursor and laggard behavior are derived from these dyadic precursor score estimations. This model is used to analyze a network of French political blogs. The scores are compared to traditional link degree metrics. We obtain insights into the dynamics of topic participation on this network, as well as the relationship between precursor/laggard and linking behaviors. We validate and analyze results with the help of an expert on the French blogosphere. Finally, we propose possible applications to the improvement of search engine ranking algorithms

Multi-Level Modeling of Quotation Families Morphogenesis

This paper investigates cultural dynamics in social media by examining the proliferation and diversification of clearly-cut pieces of content: quoted texts. In line with the pioneering work of Leskovec et al. and Simmons et al. on memes dynamics we investigate in deep the transformations that quotations published online undergo during their diffusion. We deliberately put aside the structure of the social network as well as the dynamical patterns pertaining to the diffusion process to focus on the way quotations are changed, how often they are modified and how these changes shape more or less diverse families and sub-families of quotations. Following a biological metaphor, we try to understand in which way mutations can transform quotations at different scales and how mutation rates depend on various properties of the quotations.Comment: Published in the Proceedings of the ASE/IEEE 4th Intl. Conf. on Social Computing "SocialCom 2012", Sep. 3-5, 2012, Amsterdam, N

Generating constrained random graphs using multiple edge switches

The generation of random graphs using edge swaps provides a reliable method to draw uniformly random samples of sets of graphs respecting some simple constraints, e.g. degree distributions. However, in general, it is not necessarily possible to access all graphs obeying some given con- straints through a classical switching procedure calling on pairs of edges. We therefore propose to get round this issue by generalizing this classical approach through the use of higher-order edge switches. This method, which we denote by "k-edge switching", makes it possible to progres- sively improve the covered portion of a set of constrained graphs, thereby providing an increasing, asymptotically certain confidence on the statistical representativeness of the obtained sample.Comment: 15 page

Science mapping with asymmetrical paradigmatic proximity

We propose a series of methods to represent the evolution of a field of science at different levels: namely micro, meso and macro levels. We use a previously introduced asymmetric measure of paradigmatic proximity between terms that enable us to extract structure from a large publications database. We apply our set of methods on a case study from the Complex Systems Community through the mapping of more than 400 Complex Systems Science concepts indexed from a database as large as several millions of journal papers. We will first recapitulate the main properties of our asymmetric proximity measure. Then we show how salient paradigmatic fields can be embedded into a 2-dimensional visualization into which the terms are plotted according to their relative specificity and generality index. This meso-level helps us producing macroscopic maps of the field of science studied featuring the former paradigmatic fields

The Reconstruction of Science Phylogeny

We are facing a real challenge when coping with the continuous acceleration of scientific production and the increasingly changing nature of science. In this article, we extend the classical framework of co-word analysis to the study of scientific landscape evolution. Capitalizing on formerly introduced science mapping methods with overlapping clustering, we propose methods to reconstruct phylogenetic networks from successive science maps, and give insight into the various dynamics of scientific domains. Two indexes - the pseudo-inclusion and the empirical quality - are introduced to qualify scientific fields and are used for reconstruction validation purpose. Phylogenetic dynamics appear to be strongly correlated to these two indexes, and to a weaker extent, to a third one previously introduced (density index). These results suggest that there exist regular patterns in the "life cycle" of scientific fields. The reconstruction of science phylogeny should improve our global understanding of science evolution and pave the way toward the development of innovative tools for our daily interactions with its productions. Over the long run, these methods should lead quantitative epistemology up to the point to corroborate or falsify theoretical models of science evolution based on large-scale phylogeny reconstruction from databases of scientific literature

Information diffusion on realistic networks

Prix du meilleur article étudiantNational audienceLes modèles de diffusion d'information mettent traditionnellement en jeu un réseau sous-jacent dont la topologie reproduit certaines propriétés observées dans les réseaux réels. Toutefois, la comparaison des phénomènes de diffusion observés sur des réseaux générés par des modèles classiques avec ceux se produisant au sein de réseaux réels reste peu étudiée. Dans une démarche empiriste, nous proposons dans cette étude d'évaluer l'écart de comportement induit par l'utilisation de divers modèles stylisés, dont notamment certains réseaux dits ``sans-échelle''

Bottom-up scientific field detection for dynamical and hierarchical science mapping - methodology and case study

International audienceMassive collections of scientific publications are now available on-line thanks to multiple public platforms. These databases usually cover large-scale scientific production over several decades and for a broad range of thematic areas. Today researchers are used to perform queries on these databases with keywords or combination of keywords in order to find articles associated to a precise scientific field. This full text indexation performed for millions of articles represents a huge amount of public information. But instead of being used to characterize articles, can we revert the standpoint and use this information to characterize concepts neighborhood and their evolution? In this paper we give a yes answer to this question looking more precisely at the way concepts can be dynamically clustered to shed light on the way paradigm are structured. We define an asymmetric paradigmatic proximity between concepts which provide hierarchical structure to the scientific database upon which we test our methods. We also propose overlapping categorization to describe paradigms as sets of concepts that may have several usages

Socio-semantic dynamics in a blog network

The blogosphere can be construed as a knowledge network made of bloggers who are interacting through a social network to share, exchange or produce information. We claim that the social and semantic dimensions are essentially co-determined and propose to investigate the co-evolutionary dynamics of the blogosphere by examining two intertwined issues: First, how does knowledge distribution drive new interactions and thus influence the social network topology? Second, which role structural network properties play in the information circulation in the system? We adopt an empirical standpoint by analyzing the semantic and social activity of a portion of the US political blogosphere, monitored on a period of four months