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Using a complex-network perspective, this paper empirically explores the determinants of the process through which countries, given their capabilities, specialize in agricultural production. Using production data from the Food and Agriculture Organization (FAO) for the period 1993-2013, we characterize the agricultural production space as a time-sequence of bipartite networks, connecting countries to the agricultural products they produce. We then project this representation in the agricultural production spaces, linking countries or products according to their similarity in production profiles, and we identify properties and determinants underlying their evolution. We find that, despite the unprecedented pressure that food systems have been undergoing in recent years, the agricultural production space is a very dense network displaying well-defined and stable communities of countries and products. We also show that the observed country community structures are not only shaped by environmental conditions, but also by economic, socio-political, and technological factors. We conclude by discussing the implications of such findings on our understanding of the complex relationships involving production capabilities and specialization patterns. Fil: Campi, Mercedes Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Interdisciplinario de Economía Política de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Económicas. Instituto Interdisciplinario de Economía Política de Buenos Aires; Argentina Fil: Dueñas, Marco. Universidad de Bogota Jorge Tadeo Lozano; Colombia Fil: Fagiolo, Giorgio. Scuola Superiore Sant' Anna; Italia

In the last years, there has been a growing interest in studying the global food system as a complex evolving network. Much of the literature has been focusing on the way countries are interconnected in the food system through international-trade linkages, and what consequences this may have in terms of food security and sustainability. Little attention has been instead devoted to understanding how countries, given their capabilities, specialize in agricultural production and to the determinants of country specialization patterns. In this paper, we start addressing this issue using FAO production data for the period 1993-2013. We characterize the food production space as a time-sequence of bipartite networks, connecting countries to the agricultural products they produce, and we identify properties and determinants underlying their evolution. We find that the agricultural product space is a very dense network, which however displays well-defined and stable communities of countries and products, despite the unprecedented pressure that food systems have been undergoing in recent years. We also find that the observed community structures are not only shaped by agro-ecological conditions but also by economic, socio-political, and technological factors. Finally, we discuss the implications that such findings may have on our understanding of the complex relationships involving country production capabilities, their specialization patterns, food security, and the nutrition content of the domestic part of their food supply.

Near real-time event streams are a key feature in many popular social media applications. These types of applications allow users to selectively follow event streams to receive a curated list of real-time events from various sources. Due to the emphasis on recency, relevance, personalization of content, and the highly variable cardinality of social subgraphs, it is extremely difficult to implement feed following at the scale of major social media applications. This leads to multiple architectural approaches, but no consensus has been reached as to what is considered to be an idiomatic solution. As of today, there are various theoretical approaches exploiting the dynamic nature of social graphs, but not all of them have been applied in practice. In this paper, large-cardinality graphs are placed in the context of existing research to highlight the exceptional data management challenges that are posed for large-scale real-time social media applications. This work outlines the key characteristics of data dissemination in large-cardinality social graphs, and overviews existing research and state-of-the-art approaches in industry, with the goal of stimulating further research in this direction.

In this paper we introduce the notion of protein interaction network. This is a graph whose vertices are the protein-s amino acids and whose edges are the interactions between them. Using a graph theory approach, we observe that according to their structural roles, the nodes interact differently. By leading a community structure detection, we confirm this specific behavior and describe thecommunities composition to finally propose a new approach to fold a protein interaction network., {"references":["R. Albert, H. Jeong, and A.-L. Barab'asi. The diameter of the world wide web. Nature, 401:130-131, 1999.","A. R. Atilgan, P. Akan, and C. Baysal. Small-world communication of residues and significance for protein dynamics. Biophys J, 86(1 Pt\n1):85-91, January 2004.","R. Beatson and L. Greengard. A short course on fast multipole methods.","H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat,\nH. Weissig, I. N. Shindyalov, and P. E. Bourne. The protein data bank. Nucleic Acids Research, 28:235-242, 2000.","C. Branden and J. Tooze. Introduction to protein structure. Garland\nPublishing, 1999.","K. V. Brinda and S. Vishveshwara. A network representation of protein\nstructures: implications for protein stability. Biophys J, 89(6):4159-\n4170, December 2005.","A. Broder, R. Kumar, F. Maghoul, P. Raghavan, S. Rajagopalan, R. Stata,\nA. Tomkins, and J. Wiener. Graph structure in the Web. Computer Networks, 33(1-6):309-320, 2000.","Aaron Clauset, M. E. J. Newman, and Cristopher Moore. Finding\ncommunity structure in very large networks. Physical Review E,\n70:066111, 2004.","N. V. Dokholyan, L. Li, F. Ding, and E. I. Shakhnovich. Topological\ndeterminants of protein folding. Proc Natl Acad Sci U S A, 99(13):8637-\n8641, June 2002.\n[10] Thomas M. J. Fruchterman and Edward M. Reingold. Graph drawing\nby force-directed placement. Software - Practice and Experience,\n21(11):1129-1164, 1991.\n[11] O. Gaci and S. Balev. Hubs identification in amino acids interaction\nnetworks. In Proceedings of the 7th ACS/IEEE International Conference\non Computer Systems and Applications, 2009. 7 pages.\n[12] O. Gaci and S. Balev. The small-world model for amino acid interaction\nnetworks. In Proceedings of the IEEE AINA 2009, workshop on\nBioinformatics and Life Science Modeling and Computing, 2009. 6\npages.\n[13] A. Ghosh, K. V. Brinda, and S. Vishveshwara. Dynamics of lysozyme\nstructure network: probing the process of unfolding. Biophys J,\n92(7):2523-2535, April 2007.\n[14] L. Greengard and V. Rokhlin. A fast algorithm for particle simulations.\nJ. Comput. Phys., 73(2):325-348, 1987.\n[15] H. Jeong, B. Tombor, R. Albert, Z. N. Oltvai, and A.-L. Barab'asi. The\nlarge-scale organization of metabolic networks. Nature, 407(6804):651-\n654, October 2000.\n[16] U. K. Muppirala and Z. Li. A simple approach for protein structure\ndiscrimination based on the network pattern of conserved hydrophobic\nresidues. Protein Eng Des Sel, 19(6):265-275, June 2006.\n[17] M. E. J. Newman. Fast algorithm for detecting community structure in\nnetworks. Physical Review E, 69:066133, 2004.\n[18] John P. Scott. Social Network Analysis: A Handbook. SAGE Publications,\nJanuary 2000.\n[19] S. Wasserman and K. Faust. Social network analysis : methods and\napplications , volume 8 of Structural analysis in the social sciences.\nCambridge University Press, Cambridge, 1994.\n[20] D. J. Watts and S. H. Strogatz. Collective dynamics of -small-world-\nnetworks. Nature., 393:440-442, 1998.\n[21] Bo Yang and Da You Liu. Incremental algorithm for detecting community structure in dynamic networks. In proceedings of the 4th International Conference on Machine Learning and Cybernetics, pages 2284-2290, August 2005."]}