Vertex routing models

A class of models describing the flow of information within networks via routing processes is proposed and investigated, concentrating on the effects of memory traces on the global properties. The long-term flow of information is governed by cyclic attractors, allowing to define a measure for the information centrality of a vertex given by the number of attractors passing through this vertex. We find the number of vertices having a non-zero information centrality to be extensive/sub-extensive for models with/without a memory trace in the thermodynamic limit. We evaluate the distribution of the number of cycles, of the cycle length and of the maximal basins of attraction, finding a complete scaling collapse in the thermodynamic limit for the latter. Possible implications of our results on the information flow in social networks are discussed.Comment: 12 pages, 6 figure

Academic team formation as evolving hypergraphs

This paper quantitatively explores the social and socio-semantic patterns of constitution of academic collaboration teams. To this end, we broadly underline two critical features of social networks of knowledge-based collaboration: first, they essentially consist of group-level interactions which call for team-centered approaches. Formally, this induces the use of hypergraphs and n-adic interactions, rather than traditional dyadic frameworks of interaction such as graphs, binding only pairs of agents. Second, we advocate the joint consideration of structural and semantic features, as collaborations are allegedly constrained by both of them. Considering these provisions, we propose a framework which principally enables us to empirically test a series of hypotheses related to academic team formation patterns. In particular, we exhibit and characterize the influence of an implicit group structure driving recurrent team formation processes. On the whole, innovative production does not appear to be correlated with more original teams, while a polarization appears between groups composed of experts only or non-experts only, altogether corresponding to collectives with a high rate of repeated interactions

Enrolling nature in the smart city: Discourses and imaginaries of Parisian smart city

Seeking for optimised resources exploitation, efficient flows management, sustainable environmental impacts, smart city programs are enrolling and repurposing in their networked technological infrastructure a vast array of objects, not least natural ones: trees become connected to reduce “heat islands”; algae are pushed into street furniture to absorb pollutants; mushrooms, strawberries, aromatic plants and vegetables are grown - up over roofs, down in former underground parking lots, vertically grafting on wall - to satisfy local food demands; sheeps and muttons are transformed into lawn mower. All these examples came from a rather specific implementation of the smart city. They take place in Paris, as pilots, experiments, tests and demos, attempting to blend together the biological and the socio-technical layer of the city, along with the historically rooted aesthetic of the city with a future projected imaginations. To observe and describe them, we extended an ongoing research concerned with the issues raised by urban nature in Paris: the NATURPRADI project. We mapped the discursive and pictorial elements of the Parisian urban nature debate by tracing digital-native contents produced on Twitter from July 2016 till July 2017. This digital method approach led to a series of visualisations showing how objects, places, practices and technologies are mobilised, re-appropriated by the smart city. By exploring the visualisations, we intend to provide a empirical base to discuss the frictions provoked by the articulations of nature, technology and the city in business models, policy toolboxes and citizens engagement initiatives

Fundamental insights on when social‐network data are most critical for conservation planning

As declines in biodiversity accelerate, there is an urgent imperative to ensure that every dollar spent on conservation counts towards species protection. Systematic conservation planning is a widely used approach to achieve this, but there is growing concern that it must better integrate the human social dimensions of conservation to be effective. Yet, we lack fundamental insights about when social data is most critical to inform conservation planning decisions. We addressed this by deriving novel principles to guide strategic investment in social network information for systematic conservation planning. We considered the common conservation problem of identifying which social actors, in a social network, to engage with to incentivize conservation behavior that maximizes the number of species protected. Using simulations of social networks and species distributed across network nodes, we found the optimal state‐dependent strategies and the value of social network information. We did this for a range of ‘motif’ networks structures and species distributions and applied the approach to a small‐scale fishery in Kenya. We find that the value of social network information depends strongly on both the distribution of species and social network structure. When species distributions are highly nested (i.e., species poor sites are subsets of species rich sites), the value of social network information is almost always low. This suggests that information on how species are distributed across a network is critical information for determining whether to invest in collecting social network data. On the other hand, the value of social network information is greatest when social networks are highly centralized. Results for the small‐scale fishery were consistent with the simulations. This provides new insights into when to prioritize the strategic collection of social data based on a priori knowledge of species distributions and structure of the social system