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.

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In this paper, we analyze the dependency between centrality and individual performance in socially-inspired problemsolving 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.

This paper presents an efficient architecture for Motion Estimation (ME) based on the Diamond Search (DS) Algorithm. This architecture also includes the Motion Compensation (MC) for luminance samples, reusing internal ME results and avoiding the additional external memory accesses for the MC operation. The proposed architecture also generates inputs for a Fractional Motion Estimation (FME) for quarter sample precision, as proposed by the H.264/AVC standard. The main goal of this work is to design a low cost ME architecture for the DS algorithm with reduced number of external memory accesses and ready to be integrated with a fractional interpolator. The designed architecture was synthesized to Altera Stratix 4 family of FPGAs and in the worst case scenario, this architecture is able to process Full HD (1080p) videos in real time.

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