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In this paper we evaluate the effect of noise on community scoring and centrality-based parameters with respect to two different aspects of network analysis: (i) sensitivity, that is how the parameter value changes as edges are removed and (ii) reliability in the context of message spreading, that is how the time taken to broadcast a message changes as edges are removed. Our experiments on synthetic and real-world networks and three different noise models demonstrate that for both the aspects over all networks and all noise models, permanence qualifies as the most effective metric. For the sensitivity experiments closeness centrality is a close second. For the message spreading experiments, closeness and betweenness centrality based initiator selection closely competes with permanence. This is because permanence has a dual characteristic where the cumulative permanence over all vertices is sensitive to noise but the ids of the top-rank vertices, which are used to find seeds during message spreading remain relatively stable under noise. 

Analyzing large dynamic networks is an important problem with applications in a wide range of disciplines. A key operation is updating the network properties as its topology changes. In this paper we present graph sparsification as an efficient abstraction for updating the properties of dynamic networks. We demonstrate the applicability of graph sparsification in updating the connected components in random and scalefree networks on shared memory systems. Our results show that the updating is scalable (10X on 16 processors for larger networks). To the best of our knowledge this is the first parallel implementation of graph sparsification. Based on these initial results, we discuss how the current implementation can be further improved and how graph sparsification can be applied to updating other network properties.