Abstract: Various exemplary embodiments relate to a method of determining the root cause of service degradation in a network, the method including determining a window of time; determining one or more abnormal Key Quality Indicators (KQIs) in the window; determining one or more abnormal Key Performance Indicators (KPIs) in the window; calculating a conditional probability that each of one or more KPIs is abnormal when a Key Quality Indicator (KQI) is normal; calculating a conditional probability that the each of one or more KPIs is abnormal when the KQI is abnormal; calculating a score for each KPI based upon a divergence of a Beta distribution for the conditional probability that each of one or more KPIs is abnormal when a KQI is normal and a Beta distribution for the conditional probability that the each of one or more KPIs is abnormal when the KQI is abnormal; and generating a representative root-cause list based upon the score for each KPI.

Abstract: Various exemplary embodiments relate to a method of determining the root cause of service degradation in a network, the method including determining a window of time; determining one or more abnormal Key Quality Indicators (KQIs) in the window; determining one or more abnormal Key Performance Indicators (KPIs) in the window; calculating a conditional probability that each of one or more KPIs is abnormal when a Key Quality Indicator (KQI) is normal; calculating a conditional probability that the each of one or more KPIs is abnormal when the KQI is abnormal; calculating a score for each KPI based upon a divergence of a Beta distribution for the conditional probability that each of one or more KPIs is abnormal when a KQI is normal and a Beta distribution for the conditional probability that the each of one or more KPIs is abnormal when the KQI is abnormal; and generating a representative root-cause list based upon the score for each KPI.

Abstract: A method for deriving a composite tie metric for an edge between nodes of a telecommunication call graph includes receiving descriptive data with original values for descriptive attributes associated with a telecommunication call graph formed by edges between nodes. Each edge relates to two nodes. Each original value relates to an edge and a descriptive attribute forming an edge-attribute pair for the corresponding original value. The descriptive data is stored in a local storage device. Scaling factors for each descriptive attribute are determined taking into account a distribution of the original values for the corresponding descriptive attribute and a common base for the descriptive attributes. Weighting factors are determined for each descriptive attribute. The composite tie metric is computed for an edge based on the original value, scaling factor, and weighting factor for the descriptive attributes.

Abstract: A system and method for generating a subscriber churn prediction includes receiving call detail records from a network operator detailing communication between subscribers of the network operator and determining tie-strengths between subscribers based on the call detail records. The system and method further includes generating a net churn influence accumulated at each subscriber from the tie-strengths by propagating churner influence between subscribers due to churn events.

Abstract: A system and method for generating a subscriber churn prediction includes receiving call detail records from a network operator detailing communication between subscribers of the network operator and determining tie-strengths between subscribers based on the call detail records. The system and method further includes generating a net churn influence accumulated at each subscriber from the tie-strengths by propagating churner influence between subscribers due to churn events.

Abstract: A method for deriving a composite tie metric for an edge between nodes of a telecommunication call graph includes receiving descriptive data with original values for descriptive attributes associated with a telecommunication call graph formed by edges between nodes. Each edge relates to two nodes. Each original value relates to an edge and a descriptive attribute forming an edge-attribute pair for the corresponding original value. The descriptive data is stored in a local storage device. Scaling factors for each descriptive attribute are determined taking into account a distribution of the original values for the corresponding descriptive attribute and a common base for the descriptive attributes. Weighting factors are determined for each descriptive attribute. The composite tie metric is computed for an edge based on the original value, scaling factor, and weighting factor for the descriptive attributes.