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首页> 外文会议>Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining >Estimating influence of social media users from sampled social networks
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Estimating influence of social media users from sampled social networks

机译：通过抽样的社交网络估算社交媒体用户的影响力

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摘要

Several indices for estimating the influence of social media users have been proposed. Most such indices are obtained from the topological structure of a social network that represents relations among social media users. However, several errors are typically contained in such social network structures because of missing data, false data, or poor node/link sampling from the social network. In this paper, we investigate the effects of node sampling from a social network on the effectiveness of indices for estimating the influence of social media users. We compare the estimated influence of users, as obtained from a sampled social network, with their actual influence. Our experimental results show that using biased sampling methods, such as sample edge count, is a more effective approach than random sampling for estimating user influence, and that the use of random sampling to obtain the structure of a social network significantly affects the effectiveness of indices for estimating user influence, which may make indices useless.
机译：已经提出了几种用于估计社交媒体用户的影响的指标。大多数这样的索引是从代表社交媒体用户之间关系的社交网络的拓扑结构获得的。但是，由于缺少数据，错误数据或来自社交网络的不良节点/链接采样，此类社交网络结构中通常包含一些错误。在本文中，我们调查了从社交网络进行节点采样对评估社交媒体用户影响的指标有效性的影响。我们将从抽样的社交网络获得的用户的估计影响与他们的实际影响进行比较。我们的实验结果表明，使用有偏抽样方法（例如样本边缘计数）比随机抽样更有效地估算用户影响力，并且使用随机抽样获取社交网络的结构会显着影响指标的有效性用于估计用户影响力，这可能会使索引无用。

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