Abstract: Is there a common path of development for different countries, or each one must follow its own way? In order to produce cars, one has to learn how to produce wheels before? Let us represent countries as walkers in a network made of goods, defined such that if a country steps on one product, it will export it. Obviously, paths can be very different: while Germany has already explored much of the available space, underdeveloped countries have a long road ahead. Which are the best paths in the product network? To answer these questions we build a network of products starting from the UN-Comtrade data about the international trade flows over time. A possible approach is to connect two products if many countries produce both of them. Wanting to study the countries’ dynamics, we want also our links to indicate if one product is necessary to produce the other, like transistors for smartphones and wheels for cars. So our network is directed: a country usually goes from one product to another, but not vice versa. We introduce an algorithm that, starting from the empirical bipartite country-product network, is able to extract this kind of information. In particular, we project the bipartite network onto a filtered monopartite one in which a suitable normalization takes into account the nested structure of the system. We find that countries follow the direction of the links during industrialization. In other words, we are able to spot which products are helpful to start to export new products. These results suggest paths in the product network which are easier to achieve, and so can drive countries’ policies in the industrialization process and to exit from the poverty trap. Reference: Zaccaria, A., Cristelli, M., Tacchella, A., and Pietronero, L., PloS one, 9(12), e113770 (2014).

Abstract: Because real-world marketing experiments are costly, firms make use of diffusion models to decrease uncertainty. Over the last few years Agent Based Models of Percolation have received increased attention in the literature, in which information about the existence of an innovation propagates through neutral Word-of-Mouth (WOM) between adopters and their susceptible neighbors, and product (e.g. price or quality) and promotion (seeding) strategies can be experimented with (cf. Solomon et al., 2000). A limitation of the basic percolation model such as Solomon et al. (2000) is that actors only receive WOM, but their attitude towards adoption remains unaffected by Positive- and Negative Word-of-Mouth (PWOM and NWOM). Although the effects of PWOM and NWOM have been studied empirically, only few extensions on the basic percolation model have been made capturing these effects (e.g. models on NWOM by Erez et al. (2004), and social reinforcement by Mas Tur (2016)). Addressing this gap, I will extend the standard percolation model by including the effect of NWOM in an actor’s decision process (from neighboring rejecters). With this model I will simulate percolation on small-world networks and test the effectiveness of price and seeding strategies to overcome the effects of NWOM on percolation size. As the relationship between price and diffusion size is highly non-linear, at some price (the percolation threshold) a small change causes the network to shift from almost no diffusion to almost full diffusion. However, NWOM may hamper percolation and an increase of seeds may prove to be more effective than lowering the price. A further contribution will be a model where awareness not only propagates from adopters but from rejecters as well, as it can be assumed that ‘negative’ information might also inform actors. Although the network will be fully informed, the effect from NWOM on percolation size may be substantially larger.

Abstract: Seminal papers are usually recognized as such only many years after publication. Citation-based indicators of paper impact share this lag and often implicitly penalize recent papers that had less time to attract citations and thus cannot score well. Using insights from complex network analysis, we introduce a new article-level metric which allows us to early identify the papers that later become highly regarded. This metric – called rescaled PageRank score - is based on combining the classical PageRank centrality metric with the explicit requirement that paper score is not biased by paper age. We analyze here the network of citations among the 449935 papers published by the American Physical Society journals between 1893 and 2009, and focus on a group of papers labeled as Milestone Letters by the editors of Physical Review Letters, a leading physics journal. We compare various metrics with respect to their ability to identify the milestone papers and show that rescaled PageRank score outperforms the other metrics. The performance gap between rescaled PageRank and PageRank is particularly wide in the first years after paper publication, and it takes 15 years for PageRank to reach the identification level of the rescaled score. Due to its ability to recognize high-quality papers earlier than other metrics, rescaled PageRank score could prove particularly useful for the evaluation of young researchers, who are disadvantaged by indices biased by age and may be forced to leave academia if their potential is not appreciated promptly enough. The score proposed here may find further applications in other contexts, such as early identification of viral content or high-quality websites in the World Wide Web.