This paper examines the impact of heavy churn on the robustness of decentralized social network-based Sybil defense (SNSD) schemes. Our analysis reveals that (i) heavy churn disintegrates the social overlay network that is fundamental to these schemes into multiple disconnected components, resulting in poor network connectivity, and (ii) a naive solution that adds links from each node to all its 2-hop neighbors improves network connectivity but comes at a significant cost of poor attack resilience of these schemes.

We propose a new design point in the trade-off between network connectivity and attack resilience of SNSD schemes, where each node adds links to only a selective few of all its 2-hop neighbors based on a minimum expansion contribution (MinEC) heuristic. Extensive evaluation through simulations shows that our approach fares as good as the naive 2-hop solution in terms of network connectivity, while making little compromise on the attack resilience. Moreover, our approach preserves the fast-mixing property that is fundamental to many SNSD schemes even at high levels of churn. This result suggests that existing and potential future SNSD schemes relying on this property can incorporate our approach into their designs with minimal changes.
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Due to increasingly large datasets, graph analytics - traversals, all-pairs shortest path computations, centrality measures, etc. - are becoming the focus of high-performance computing (HPC). Because HPC is currently dominated by many-core architectures (both CPUs and GPUs), new graph processing solutions have to be defined to efficiently use such computing resources. Prior work focuses on platform-specific performance studies and on platform-specific algorithm development, successfully proving that algorithms highly tuned to GPUs or multi-core CPUs can provide high performance graph analytics. However, the portability of such algorithms remains an important concern for many users, especially the many companies without the resources to invest in HPC or concerned about lock-in in single-use parallel techniques. In this work, we investigate the functional portability and performance of graph analytics algorithms. We conduct an empirical study measuring the performance of 3 graph analytics algorithms (a single code implemented in OpenCL and targeted at many-core CPUs and GPUs), on 3 different platforms, using 11 real-world and synthetic datasets. Our results show that the code is functionally portable, that is, applications can run unchanged on both CPUs and GPUs. The large variation in their observed performance indicates that portability is necessary not only for productivity, but, surprisingly, also for performance. We conjecture that the impact of datasets on performance is too high to allow platform-specific algorithms to outperform the portable algorithms by large margins, in all cases. Our conclusion is that portable parallel graph analytics is feasible without significant performance loss, and provides a productive alternative to the expensive trial-and-error selection of one algorithm for each (graph,platform) pair. @en