Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> Data Mining. <s> We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2 million customers and by analysing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad hoc modular networks. <s> BIB001 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> Data Mining. <s> In wireless cellular system, due to the convergence of user behavior, there usually exists some typical scenarios which exhibit different traffic patterns, e.g. stadium, campus and central business district (CBD). Accurate traffic scenario recognition and analysis will lead to more efficient resource management and better QoS (quality-of-service) provision. In this paper, with the idea of social network analysis, a base station social network (BSSN) based method is proposed to recognize and analyze the typical traffic scenarios in wireless cellular system. Firstly, we construct a BSSN to visualize the hidden relationship between base stations (BSs) by using previously measured spatial-temporal wireless traffic data. Then, a modularity optimization based method is used to discover community structure in BSSN. Analytical results show that each community in BSSN corresponds to a typical wireless scenario with a unique traffic pattern in cellular system. Experimental results illustrate that our proposed BSSN based method is general enough and achieves satisfactory performance in traffic scenario recognition and analysis. <s> BIB002
The process of turning raw data into useful information, such as discovering patterns in large datasets, is referred to as data mining (DM). Due to the large amounts of high quality data available in the mobile industry, mobile operators can learn about the behavior of their customers and develop effective marketing strategies. The datasets that can be used to this end include call detail records, which contain detailed information for each call made, and mobility traces, that is, the sequence of serving cells. In mobile communication systems, due to the convergence of user behavior, there usually exists some typical scenarios which exhibit different traffic patterns, for example, stadium, campus, special (or big) events, and central business district (CBD). Therefore, accurate traffic scenario recognition and analysis are expected to lead to more efficient resource management and better QoE provision. The authors in BIB002 used the Louvain method BIB001 (a widely used method for detecting communities in large networks) for recognizing and analyzing the typical scenarios in wireless cellular networks. In this paper, a modularity optimization based method is used to discover the community structure, where researchers utilize previously measured spatialtemporal wireless traffic datasets. The obtained experimental results show that the proposed method can acquire satisfactory performance in traffic scenario recognition and analysis, which can lead to the development of efficient resource allocation schemes.