Customer Ranking Authority-Hub Algorithm for Mobile Communications in China

Wu, Sen; Wu, Lijun; Jin, Haiyan; Gu, Shujuan

doi:10.1109/fskd.2007.234

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…There are many related works which have been presented in SNA, like finding of communities, alpha users within the communities, role player, hub member and non-hub members' identification (Kleinberg 1999;Wu et al 2007;Guimera et al 2007). Faced initially with a mobile social network comprising hundreds of millions of nodes and a few billion edges, our intention was to split the network into smaller groups in order of magnitude and we expect it facilitates an improved performance on targeted advertisements.…”

Section: Introductionmentioning

confidence: 99%

Analyzing and labeling telecom communities using structural properties

Saravanan¹,

Prasad²,

Karishma³

et al. 2011

Soc. Netw. Anal. Min.

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Social network analysis and mining has been highly influenced by the online social web sites, telecom consumer data and instant messaging systems and has widely analyzed the presence of dense communities using graph theory and machine learning techniques. Mobile social network analysis is the mapping and measuring of interactions and flows between people, groups, and organizations based on the usage of their mobile communication services. Community identification and mining is one of the recent major directions in social network analysis. In this paper we find the communities in the network based on a modularity factor. Then we propose a graph theory-based algorithm for further split of communities resulting in smaller sized and closely knit sub-units, to drill down and understand consumer behavior in a comprehensive manner. These sub-units are then analyzed and labeled based on their group behavior pattern. The analysis is done using two approaches:-rule-based, and cluster-based, for comparison and the able usage of information for suitable labeling of groups. Moreover, we measured and analyzed the uniqueness of the structural properties for each small unit; it is another quick and dynamic way to assign suitable labels for each distinct group. We have mapped the behavior-based labeling with unique structural properties of each group. It reduces considerably the time taken for processing and identifying smaller sub-communities for effective targeted marketing. The efficiency of the employed algorithms was evaluated on a large telecom dataset in three different stages of our work.

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Section: Introductionmentioning

confidence: 99%

Analyzing and labeling telecom communities using structural properties

Saravanan¹,

Prasad²,

Karishma³

et al. 2011

Soc. Netw. Anal. Min.

View full text Add to dashboard Cite

show abstract

“…Consequently, SNA is a set of research procedures for identifying group of people who shared common structures in systems based on the existing relationship among actors. SNA consists of several related works such as alpha users within the communities, community detection, hub member and non-hub members' identification [2] among others.…”

Section: Introductionmentioning

confidence: 99%

Social Opinion Network Analytics in Community Based Customer Churn Prediction

Ibitoye¹,

Onifade²

2022

Journal on Big Data

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Community based churn prediction, or the assignment of recognising the influence of a customer's community in churn prediction has become an important concern for firms in many different industries. While churn prediction until recent times have focused only on transactional dataset (targeted approach), the untargeted approach through product advisement, digital marketing and expressions in customer's opinion on the social media like Twitter, have not been fully harnessed. Although this data source has become an important influencing factor with lasting impact on churn management. Since Social Network Analysis (SNA) has become a blended approach for churn prediction and management in modern era, customers residing online predominantly and collectively decide and determines the momentum of churn prediction, retention and decision support. In existing SNA approaches, customers are classified as churner or non-churner (1 or 0). Oftentimes, the customer's opinion is also neglected and the network structure of community members are not exploited. Consequently, the pattern and influential abilities of customers' opinion on relative members of the community are not analysed. Thus, the research developed a Churn Service Information Graph (CSIG) to define a quadruple churn category (churner, potential churner, inertia customer, premium customer) for non-opinionated customers via the power of relative affinity around opinionated customers on a direct node to node SNA. The essence is to use data mining technique to investigate the patterns of opinion between people in a network or group. Consequently, every member of the online social network community is dynamically classified into a churn category for an improved targeted customer acquisition, retention and/or decision supports in churn management.

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Social Network Analysis for Churn Prediction in Telecom Data

Pushpa¹,

Shobha²

2016

IJCCT

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Social Network Analysis (SNA) is a set of research procedures for identifying group of people who share common structures in systems based on the relations among actors. Grounded in graph and system theories, this approach has proven to be powerful measures for studying networks in various industries like Telecommunication, banking, physics and social world, including on the web. Since Telecommunication industries deals with huge amount of data, manual analysis of data is very difficult. In this paper we explore the Social Network Analysis techniques for Churn Prediction in Telecom data. Typical work on social network analysis includes the construction of multi-relational telecom social network and centrality measures for prediction of churners in telecom social network.

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Customer Ranking Authority-Hub Algorithm for Mobile Communications in China

Cited by 3 publications

References 10 publications

Analyzing and labeling telecom communities using structural properties

Analyzing and labeling telecom communities using structural properties

Social Opinion Network Analytics in Community Based Customer Churn Prediction

Social Network Analysis for Churn Prediction in Telecom Data

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