Temporality in Link Prediction: Understanding Social Complexity
Temporality in Link Prediction: Understanding Social Complexity
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发表时间：
2009
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通讯作者：
Anet Potgieter;K. April;Richard J. E. Cooke;I. Osunmakinde
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作者：
Anet Potgieter;K. April;Richard J. E. Cooke;I. Osunmakinde
本文描述了对社交网络中突发时间现象的发现和建模的研究。它总结了汇集当代科学中两种观点的实验结果：贝叶斯分析和链接预测，以增强当前对社会网络分析（SNA）中出现的时间模式的理解，特别是通过社会联系创造价值——管理科学中一个重要且不断发展的学科。传统的链接预测方法使用图表中的度量值来确定新链接可能出现的位置，并且在分析长期图表趋势方面所做的工作很少。我们发现现有的图生成模型的预测不切实际，在某些网络的研究中可以通过使用时间度量来补充。迄今为止，链接预测研究中还没有使用时间信息，从而排除了社交图序列中出现的有价值的时间趋势，也降低了链接预测的准确性。我们从 Pussokram 在线约会网络数据集中提取信息，每个类别形成 9,939 个案例。 Weka数据挖掘系统中的逻辑回归用于执行链接预测。我们的结果表明，时间度量是对链接预测极其有价值的新贡献，应该在未来的应用中使用。除了使用指标来衡量社交网络中参与者的局部行为之外，我们还使用贝叶斯网络将指标之间的相互关系建模为局部行为，并将个体之间形成的链接建模为紧急行为（社会复杂性）。我们还使用动态贝叶斯网络 (DBN) 探索了指标如何随时间演变。 （ProQuest：...表示省略了公式。） 简介 社交网络是复杂的系统，其特征是大量互连的组成实体以及这些实体之间的高度交互。这种网络中的相互关系是动态的并且随着时间的推移而演变。社交网络的时间变化很难理解和预测。社交网络中的组成实体及其全局行为之间的相互关系可能如此之多，而且大多是隐藏的，并且可能影响整个社交网络中如此多的不同实体，以至于变得极其难以理解。复杂性理论非常适合研究社交网络。复杂自适应系统理论是复杂性理论的一个分支，它研究由能够共同进化以响应环境变化的主体组成的系统。这种系统中的代理不断地对环境中其他代理的行为和事件做出反应。社交网络是一个复杂的自适应系统，其中人们是彼此交互的代理。为了理解社会的复杂性，必须理解参与者的局部行为，以及他们如何共同行动并与环境相互作用以形成整体。为了对此进行建模，我们使用贝叶斯网络技术来挖掘和建模社交网络中局部行为和全局行为之间随着时间的推移而出现的关系。社会复杂性任何社会组织的复杂结构都可以被认为是个人/代理的网络（Nohria 8c Eccles，1992：288；Lincoln，1982），有时被称为网络参与者，它在一个环境中运行和运行，而该环境本身就是其他分布式组织的环境（Van Wijk 等，2003；Potgieter 等，2006），并且网络内代理的行为被塑造和影响。由于自己的地位和地位而受到限制 网络的嵌入性（Nohria，1992）。复杂自适应系统理论是复杂性理论的一个分支，非常适合研究社交网络，它研究由能够集体进化以响应环境变化的代理组成的系统（即......
This article describes research into the discovery and modelling of emergent temporal phenomena in social networks. It summarizes experimental results that bring together two views in contemporary science: Bayesian analysis and link prediction, to enhance the current understanding of emergent temporal patterns in social network analysis (SNA), particularly in value creation through social connectedness-an important, and growing, discipline within management science. Traditional link prediction methods use the values of metrics in a graph to determine where new links are likely to arise, and little work has been done on analyzing long-term graph trends. We have found that existing graph generation models are unrealistic in their prediction, and can be complemented through the use of temporal metrics, in the study of some networks. To date, no temporal information has been used in link prediction research, thereby excluding valuable temporal trends that emerge in sociogram sequences and also lowering the accuracy of the link prediction. We extracted information from the Pussokram online dating network dataset, and 9,939 cases of each class were formed. Logistic regression in the Weka data mining system was used to perform link prediction. Our results show that temporal metrics are an extremely valuable new contribution to link prediction, and should be used in future applications. In addition to using metrics to measure the local behaviors of participants in social networks, we used Bayesian networks to model the interrelationships between the metrics as local behaviors and links forming between individuals as emergent behaviors (social complexity). We also explored how the metrics evolve over time using Dynamic Bayesian Networks (DBN). (ProQuest: ... denotes formulae omitted.) Introduction Social networks are complex systems that are characterized by high numbers of interconnected component entities, and a high degree of interaction between these entities. The interrelationships in such a network are dynamic and evolve over time. Temporal changes in social networks are difficult to understand and anticipate. The interrelationships between the component entities in a social network and its global behavior can be so numerous and mostly hidden, and can affect so many different entities throughout the social network that it becomes extremely difficult to comprehend. Complexity theory is ideally suited to study social networks. Complex adaptive systems theory is a branch of complexity theory that studies systems that consist of agents that are collectively able to evolve in response to environmental changes. The agents in such a system constantly act and react to the actions of other agents and events in the environment. A social network is a complex adaptive system, in which people are agents interacting with each other. In order to understand social complexity, the local behaviors ofthe participants must be understood, as well as how they act together and interact with the environment to form the whole. To model this, we use Bayesian network techniques to mine and model emergent relationships between local behaviors and global behaviors in social networks over time. Social Complexity The complex structure of any social organization can be thought of as a network of individuals/agents (Nohria 8c Eccles, 1992: 288; Lincoln, 1982), sometimes termed network actors, that operates and is operated on in an environment which itself is an environment of other distributed organizations (Van Wijk et al, 2003; Potgieter et al, 2006), and the actions of agents within the network are shaped and constrained because of their position and embeddedness in the network (Nohria, 1992). Complex adaptive systems theory, a branch of complexity theory that is well suited to study social networks, investigates systems that consist of agents that are collectively able to evolve in response to environmental changes (i. …