Parallel Graph Partitioning for Complex Networks

Processing large complex networks like social networks or web graphs has recently attracted considerable interest. In order to do this in parallel, we need to partition them into pieces of about equal size. Unfortunately, previous parallel graph partitioners originally developed for more regular mesh-like networks do not work well for these networks. This paper addresses this problem by parallelizing and adapting the label propagation technique originally developed for graph clustering. By introducing size constraints, label propagation becomes applicable for both the coarsening and the refinement phase of multilevel graph partitioning. We obtain very high quality by applying a highly parallel evolutionary algorithm to the coarsened graph. The resulting system is both more scalable and achieves higher quality than state-of-the-art systems like ParMetis or PT-Scotch. For large complex networks the performance differences are very big. For example, our algorithm can partition a web graph with 3.3 billion edges in less than sixteen seconds using 512 cores of a high performance cluster while producing a high quality partition -- none of the competing systems can handle this graph on our system.Comment: Review article. Parallelization of our previous approach arXiv:1402.328

CH-FARMIS 2.0: A Sector-Model to Assess the Economic and Environmental Impacts of Swiss Direct Payment Schemes

Quantitative sector models are an essential tool to forecast the sector-wide economic effects of agricultural policies. However, to cover ecological effects with economic sector models is still a methodological challenge. This poster describes a promising approach, which is currently implemented at the Research Institute of Organic Agriculture (FiBL) to meet this challenge. CH-FARMIS is a sector consistent farm group model, which is able to forecast economic impacts of policies on farm, regional and sector-level. In a flexible way, CH-FARMIS is able to split up the farm sector in farm groups according to various characteristics (region, farming system, farm type, etc.). CH-FARMIS is based on positive mathematical programming (PMP) to allow for a realistic reproduction of the Swiss agricultural sector. Currently, CH-FARMIS is augmented in two ways in order to illustrate the environmental impacts of farmers’ decisions and land use: Firstly, intensity levels of farm production are introduced. Secondly, all farm activities and their intensity levels are equipped with ecological indicators, which are derived from LCA data. In an iterative Delphi-procedure, polling a set of competence teams, the LCA data is adapted to the model needs and transposed from farm level to sector level. The following indicators will be implemented in CH-FARMIS 2.0: Biodiversity, water contamination (N, P) and energy use. The enlarged version of CH-FARMIS will be a flexible tool which can be used for both exante assessments of policy reforms and the quantitative evaluation of single direct payments. CH-FARMIS 2.0 is planned to be operable in mid 2008

Incorporating Road Networks into Territory Design

Given a set of basic areas, the territory design problem asks to create a predefined number of territories, each containing at least one basic area, such that an objective function is optimized. Desired properties of territories often include a reasonable balance, compact form, contiguity and small average journey times which are usually encoded in the objective function or formulated as constraints. We address the territory design problem by developing graph theoretic models that also consider the underlying road network. The derived graph models enable us to tackle the territory design problem by modifying graph partitioning algorithms and mixed integer programming formulations so that the objective of the planning problem is taken into account. We test and compare the algorithms on several real world instances