Interest communities and flow roles in directed networks: the Twitter network of the UK riots.
Interest communities and flow roles in directed networks: the Twitter network of the UK riots.
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DOI：
10.1098/rsif.2014.0940
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发表时间：
2014-12-06
期刊：
影响因子：
--
通讯作者：
Barahona M
中科院分区：
文献类型：
--
作者：
Beguerisse-Díaz M;Garduño-Hernández G;Vangelov B;Yaliraki SN;Barahona M
方向性是许多信息、能量或影响得以传递的复杂网络的一个关键要素。在这类有向网络中，分析流（而不仅仅是连接的强度）对于揭示网络的重要特征至关重要，如果忽略连接的方向，这些特征可能无法被检测到。我们在此通过对2011年英国骚乱期间最具影响力的推特用户网络的研究，展示一种基于流的社区检测方法。首先，我们使用有向马尔可夫稳定性，从兴趣社区的角度，即在其中包含并强化信息流的节点群组的角度，提取不同粗糙程度的网络描述。这些兴趣社区根据位置、职业、雇主和主题揭示了用户分组。对流的研究还使我们能够生成一种兴趣距离，从任何给定用户的有利位置来看，它提供了网络中关注度的个性化视角。其次，我们采用基于角色的相似性和新颖的松弛最小生成树算法相结合的方法，分析传入和传出的长程流的特征，结果表明网络中的用户可分为五类角色。这些流角色超越了标准的领导者/追随者二分法，并且不同于基于常规/结构等价的分类。然后我们表明，兴趣社区可归入不同的信息组织结构图，其特点是不同的用户角色组合，反映了它们内部对话的质量。我们的通用框架可用于深入了解有向网络中流是如何产生、分布、保存和消耗的。
Directionality is a crucial ingredient in many complex networks in which information, energy or influence are transmitted. In such directed networks, analysing flows (and not only the strength of connections) is crucial to reveal important features of the network that might go undetected if the orientation of connections is ignored. We showcase here a flow-based approach for community detection through the study of the network of the most influential Twitter users during the 2011 riots in England. Firstly, we use directed Markov Stability to extract descriptions of the network at different levels of coarseness in terms of interest communities, i.e. groups of nodes within which flows of information are contained and reinforced. Such interest communities reveal user groupings according to location, profession, employer and topic. The study of flows also allows us to generate an interest distance, which affords a personalized view of the attention in the network as viewed from the vantage point of any given user. Secondly, we analyse the profiles of incoming and outgoing long-range flows with a combined approach of role-based similarity and the novel relaxed minimum spanning tree algorithm to reveal that the users in the network can be classified into five roles. These flow roles go beyond the standard leader/follower dichotomy and differ from classifications based on regular/structural equivalence. We then show that the interest communities fall into distinct informational organigrams characterized by a different mix of user roles reflecting the quality of dialogue within them. Our generic framework can be used to provide insight into how flows are generated, distributed, preserved and consumed in directed networks.
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