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A framework for exploring organizational structure in dynamic social networks

机译：探索动态社交网络中组织结构的框架

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摘要

Recent research has provided promising results relating to discovering communities within a social network. We find that further representing the organizational structure of a social network is an interesting issue that helps gain better understandings of the social network. In this paper, we define a data structure, named Community Tree, to depict the organizational structure and provide a framework for exploring the organizational structure in a social network. In this framework, an algorithm, which combines a modified PageRank and Random Walk on graph, is developed to derive the community tree from the social network. In the real world, a social network is constantly evolving. In order to explore the organizational structure in a dynamic social network, we develop a tree learning algorithm, which employs tree edit distance as the scoring function, to derive an evolving community tree that enables a smooth transition between two community trees. We also propose an approach to threading communities in community trees to obtain an evolution graph of the organizational structure, by which we can reach new insights from the dynamic social network. The experiments conducted on synthetic and real dataset demonstrate the feasibility and applicability of the framework. Based on the theoretical outcomes, we further apply the proposed framework to explore the evolution of organizational structure with the 2001 Enron dataset, and obtain several interesting findings that match the context of Enron.
机译：最近的研究提供了与发现社交网络中的社区有关的有希望的结果。我们发现，进一步表示社交网络的组织结构是一个有趣的问题，有助于更好地理解社交网络。在本文中，我们定义了一个名为“社区树”的数据结构来描述组织结构，并提供了一个探索社交网络中组织结构的框架。在此框架中，开发了一种算法，该算法结合了经过修改的PageRank和图上的随机游走，以从社交网络派生社区树。在现实世界中，社交网络不断发展。为了探索动态社交网络中的组织结构，我们开发了一种树学习算法，该算法将树编辑距离作为评分函数，以得出能够在两个社区树之间平滑过渡的不断发展的社区树。我们还提出了一种在社区树中穿插社区以获取组织结构演变图的方法，通过该方法我们可以从动态社交网络中获得新见解。在综合和真实数据集上进行的实验证明了该框架的可行性和适用性。基于理论结果，我们进一步应用所提出的框架，以2001年安然数据集探索组织结构的演变，并获得一些与安然环境相匹配的有趣发现。

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