Given a network N and a set of nodes that are the starting point for the spread of misinformation across N and an integer k, in the influence blocking maximization problem the goal is to find k nodes in N as the starting point for a competing information (say, a correct information) across N such that the reach of the misinformation is minimized. In this paper we deal with a more realistic scenario for this problem where different nodes have different costs and the counter strategy has a “budget” for picking nodes for a solution. Our experimental results show that the success of a given strategy varies substantially depending on the cost function in the model. In particular, we investigate the cost function where all nodes have cost 1 and a cost function that assigns higher costs to higher degree nodes. We show that, even though strategies that perform well in these two diverse cases are very different from each other, both correlate well with simple (but different) strategies: greedily choose high degree nodes and choose nodes uniformly at random.

In this work we investigate the problem of estimating the percolation centrality of every vertex in a graph. This centrality measure quantifies the importance of each vertex in a graph going through a contagious process. It is an open problem whether the percolation centrality can be computed in O(n3-c) time, for any constant c>0. In this paper we present a ~O(m) randomized approximation algorithm for the percolation centrality for every vertex of G, generalizing techniques developed by Riondato, Upfal and Kornaropoulos. The estimation obtained by the algorithm is within ε of the exact value with probability 1- δ, for fixed constants 0 < ε,δ < 1. In fact, we show in our experimental analysis that in the case of real-world complex networks, the output produced by our algorithm is significantly closer to the exact values than its guarantee in terms of theoretical worst case analysis.

The approximation of surfaces to scattered data is an important problem encountered in a variety of scientific applications, such as reverse engineering, computer vision, computer graphics, and terrain modeling. This paper describes an automatic method for constructing smooth surfaces defined as a network of curved triangular patches. The method starts with a coarse mesh approximating the surface through triangular elements covering the boundary of the domain, then iteratively adds new points from the data set until a specified error tolerance is achieved. The resulting surface over the triangular mesh is represented by piecewise polynomial patches possessing C/sup 1/ continuity. The method has been implemented and tested on a number of real data sets.