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International audience ; In this paper we use entropy statistics to measure the synergies of knowledge exploration, knowledge exploitation, and organizational control in Hungary. We categorized the firms in terms of sub-regions, industrial sectors, and firm size. Configurational information among these distributions is the indicator of the synergy in the system. The results indicate that three regimes were generated in the Hungarian transition period with different dynamics: Budapest emerges as a knowledge-based innovation system; in the north-western regions, foreign-owned companies have induced a shift in knowledge-organization; while the system is organized in the southern-eastern regions in accordance with government expenditures.

In this paper we use entropy statistics to measure the synergies of knowledge exploration, knowledge exploitation, and organizational control in Hungary. We categorized the firms in terms of sub-regions, industrial sectors, and firm size. Configurational information among these distributions is the indicator of the synergy in the system. The results indicate that three regimes were generated in the Hungarian transition period with different dynamics: Budapest emerges as a knowledge-based innovation system; in the north-western regions, foreign-owned companies have induced a shift in knowledge-organization; while the system is organized in the southern-eastern regions in accordance with government expenditures.

AbstractViral marketing campaigns target primarily those individuals who are central in social networks and hence have social influence. Marketing events, however, may attract diverse audience. Despite the importance of event marketing, the influence of heterogeneous target groups is not well understood yet. In this paper, we define the Audience Selection (AS) problem in which different sets of agents need to be evaluated and compared based on their social influence. A typical application of Audience selection is choosing locations for a series of marketing events. The Audience selection problem is different from the well-known Influence Maximization (IM) problem in two aspects. Firstly, it deals with sets rather than nodes. Secondly, the sets are diverse, composed by a mixture of influential and ordinary agents. Thus, Audience selection needs to assess the contribution of ordinary agents too, while IM only aims to find top spreaders. We provide a systemic test for ranking influence measures in the Audience Selection problem based on node sampling and on a novel statistical method, the Sum of Ranking Differences. Using a Linear Threshold diffusion model on two online social networks, we evaluate eight network measures of social influence. We demonstrate that the statistical assessment of these influence measures is remarkably different in the Audience Selection problem, when low-ranked individuals are present, from the IM problem, when we focus on the algorithm's top choices exclusively.

Social connections that span across diverse urban neighborhoods can support prosperity by mobilizing social capital. However, there is limited evidence on the spatial structure of individual social capital inside cities. This paper demonstrates that social capital measured by online social connections is spatially more concentrated for residents of lower-income neighborhoods than for residents of higher-income neighborhoods. We map the micro-geography of individual online social networks in the 50 largest metropolitan areas of the United States using a large-scale geolocalized Twitter dataset. We analyze the spatial dimension of individual social capital by the share of friends, closed triangles, and share of supported ties within circles of short distance radii (1, 3, 5, and 10 km) around users' home location. We compare residents from below-median income neighborhoods with above-median income neighborhoods, and find that users living in relatively poorer neighborhoods have a significantly higher share of connections in close proximity. Moreover, their network is more cohesive and supported within a short distance from their home. These patterns prevail across the 50 largest US metropolitan areas with only a few exceptions. The found disparities in the micro-geographic concentration of social capital can feed segregation and income inequality within cities constraining social circles of low-income residents.