Measures of complex network analysis, such as vertex centrality, have the potential to unveil existing network patterns and behaviors. They contribute to the understanding of networks and their components by analyzing their structural properties, which makes them useful in several computer science domains and applications. Unfortunately, there is a large number of distinct centrality measures and little is known about their common characteristics in practice. By means of an empirical analysis, we aim at a clear understanding of the main centrality measures available, unveiling their similarities and differences in a large number of distinct social networks. Our experiments show that the vertex centrality measures known as information, eigenvector, subgraph, walk betweenness and betweenness can distinguish vertices in all kinds of networks with a granularity performance at 95%, while other metrics achieved a considerably lower result. In addition, we demonstrate that several pairs of metrics evaluate the vertices in a very similar way, i.e. their correlation coefficient values are above 0.7. This was unexpected, considering that each metric presents a quite distinct theoretical and algorithmic foundation. Our work thus contributes towards the development of a methodology for principled network analysis and evaluation.

Recent studies have suggested that current agent-based models are not sufficiently sophisticated to reproduce results achieved by human collaborative learning and reasoning. Such studies suggest that humans are diverse and dynamic when solving problems socially. However, despite their relevance to problem-solving, these two behavioral features have not yet been fully investigated. In this paper we analyse a recent social problem-solving model and attempt to address its shortcomings. Specifically, we investigate the effects of separating exploitation from exploration in agent behaviors and explore the concept of diversity in such models. We found out that diverse populations outperform homogeneous ones in both efficient and inefficient networks. Finally, we show that agent diversity is more relevant than the strategic behavioral dynamics. This work contributes towards understanding the role of diverse and dynamic behaviors in social problem-solving as well as the advancement of state-of-art social problem-solving models.

In this paper, we analyze the dependency between centrality and individual performance in socially-inspired problem-solving systems. By means of extensive numerical simulations, we investigate how individual performance in four different models correlate with four different classical centrality measures. Our main result shows that there is a high linear correlation between centrality and individual performance when individuals systematically exploit central positions. In this case, central individuals tend to deviate from the expected majority contribution behavior. Although there is ample evidence about the relevance of centrality in social problem-solving, our work contributes to understand that some measures correlate better with individual performance than others due to individual traits, a position that is gaining strength in recent studies.

Social interactions have recently been used as an inspiration for novel agent-based problem-solving models. Particle Swarm Optimization and Memetic Networks are two such algorithms. Although they draw inspiration from different real-world social systems, they both rely on the concept of a social network to regulate the internal information flow in a structured way. In this paper, we systematically investigate how a heterogeneous population composed of individuals from these two models behave as the system seeks the solution to the benchmark problems. We report on extensive numerical simulations, showing that this heterogeneous model is able to converge faster in two highly multimodal scenarios while being otherwise statistically equivalent to the original homogeneous models. Our results provide supportive evidence for the hypothesis that higher diversity in populations of problem-solvers can be beneficial and also adds a new dimension to previous heterogeneous problem-solving models.

Social collaboration has been shown to facilitate problemsolving activity in diverse sets of environments. Nevertheless, if not well designed, social and human computation systems may achieve results only similar to those of a single human subject performing a task. This scenario reflects a need for better understanding of the performance issues of human problem-solving social networks. Firstly, we propose a model for simulating social problem-solving. We then carry out several simulations with artificial agents supported by results of experiments carried out with human subjects, in order to analyse which parameters influence the performance of collaborative problem-solving social networks. We analyse the strategies humans follow when solving a problem, comparing them with alternative ones, and identify the consequences of the employed strategies in the collective performance of the social network. Our results also indicate that copying and guessing are beneficial to the performance of the social networks. We then propose mechanisms that can improve collaborative problem-solving. Finally, we show that our results lead to a methodology for the design of efficient problem-solving systems that can be applied to several kinds of collaborative social systems.