Small world yields the most effective information spreading

Spreading dynamics of information and diseases are usually analyzed by using a unified framework and analogous models. In this paper, we propose a model to emphasize the essential difference between information spreading and epidemic spreading, where the memory effects, the social reinforcement and the non-redundancy of contacts are taken into account. Under certain conditions, the information spreads faster and broader in regular networks than in random networks, which to some extent supports the recent experimental observation of spreading in online society [D. Centola, Science {\bf 329}, 1194 (2010)]. At the same time, simulation result indicates that the random networks tend to be favorable for effective spreading when the network size increases. This challenges the validity of the above-mentioned experiment for large-scale systems. More significantly, we show that the spreading effectiveness can be sharply enhanced by introducing a little randomness into the regular structure, namely the small-world networks yield the most effective information spreading. Our work provides insights to the understanding of the role of local clustering in information spreading.Comment: 6 pages, 7 figures, accepted by New J. Phy

Coordination of Decisions in a Spatial Agent Model

For a binary choice problem, the spatial coordination of decisions in an agent community is investigated both analytically and by means of stochastic computer simulations. The individual decisions are based on different local information generated by the agents with a finite lifetime and disseminated in the system with a finite velocity. We derive critical parameters for the emergence of minorities and majorities of agents making opposite decisions and investigate their spatial organization. We find that dependent on two essential parameters describing the local impact and the spatial dissemination of information, either a definite stable minority/majority relation (single-attractor regime) or a broad range of possible values (multi-attractor regime) occurs. In the latter case, the outcome of the decision process becomes rather diverse and hard to predict, both with respect to the share of the majority and their spatial distribution. We further investigate how a dissemination of information on different time scales affects the outcome of the decision process. We find that a more ``efficient'' information exchange within a subpopulation provides a suitable way to stabilize their majority status and to reduce ``diversity'' and uncertainty in the decision process.Comment: submitted for publication in Physica A (31 pages incl. 17 multi-part figures

Construction of a Pragmatic Base Line for Journal Classifications and Maps Based on Aggregated Journal-Journal Citation Relations

A number of journal classification systems have been developed in bibliometrics since the launch of the Citation Indices by the Institute of Scientific Information (ISI) in the 1960s. These systems are used to normalize citation counts with respect to field-specific citation patterns. The best known system is the so-called "Web-of-Science Subject Categories" (WCs). In other systems papers are classified by algorithmic solutions. Using the Journal Citation Reports 2014 of the Science Citation Index and the Social Science Citation Index (n of journals = 11,149), we examine options for developing a new system based on journal classifications into subject categories using aggregated journal-journal citation data. Combining routines in VOSviewer and Pajek, a tree-like classification is developed. At each level one can generate a map of science for all the journals subsumed under a category. Nine major fields are distinguished at the top level. Further decomposition of the social sciences is pursued for the sake of example with a focus on journals in information science (LIS) and science studies (STS). The new classification system improves on alternative options by avoiding the problem of randomness in each run that has made algorithmic solutions hitherto irreproducible. Limitations of the new system are discussed (e.g. the classification of multi-disciplinary journals). The system's usefulness for field-normalization in bibliometrics should be explored in future studies.Comment: accepted for publication in the Journal of Informetrics, 20 July 201

An organization that transmits opinion to newcomers

We aim to identify the conditions under which social influence enables emergence of a shared opinion orientation among members of an organization over time, when membership is subject to continuous but partial turnover. We study an intra-organizational advice network that channels social influence over time, with a flow of joiners and leavers at regular intervals. We have been particularly inspired by a study of the Commercial Court of Paris, a judicial institution whose members are peer-elected businesspeople and are partly replaced every year. We develop an agent-based simulation of advice network evolution which incorporates a model of opinion dynamics based on a refinement of Deuant's relative agreement", combining opinion with a measure of "uncertainty" or openness to social influence. We focus on the effects on opinion of three factors, namely criteria for advisor selection, duration of membership in the organization, and new members' uncertainty. We show that criteria for interlocutor choice matter: a shared opinion is sustained over time if members select colleagues at least as experienced as themselves. Convergence of opinions appears in other congurations too, but the impact of initial opinion fades in time. Duration has an impact to the extent that the longer the time spent in the group, the stronger the possibility for convergence towards a common opinion. Finally, higher uncertainty reinforces convergence while lower uncertainty leads to coexistence of multiple opinions.social influence, advice networks, intra-organizational networks, opinion dynamics, agent-based simulation