Distributed network generation based on preferential attachment in ABS

Generation of social networks using Preferential Attachment (PA) mechanism is proposed in the Barabasi-Albert model. In this mechanism, new nodes are introduced to the network sequentially and they attach to the existing nodes preferentially where the preference can be based on the degree of the existing nodes. PA is a classical model with a natural intuition, great explanatory power and interesting mathematical properties. Some of these properties only appear in large-scale networks. However generation of such extra-large networks can be challenging due to memory limitations. In this paper, we investigate a distributedmemory approach for PA-based network generation which is scalable and which avoids low-level synchronization mechanisms thanks to utilizing a powerful programming model and proper programming constructs