Abstract: A processing system may obtain a request to add at least a first feature to a feature graph database, where the request comprises a first feature ontology of the first feature, and where the first feature ontology comprises: a label of the first feature and a relationship of the first feature to a concept or to another feature. The processing system may then identify whether the first feature is a duplicate of a second feature in the feature graph database based at least upon the first feature ontology and a second feature ontology of the second feature and generate an indication of whether the first feature is a duplicate in response to the identifying.

Abstract: A processing system may apply a community detection process to a feature graph database to identify a plurality of communities of features, the feature graph database comprising: a plurality of objects, each associated with one of a feature or a concept, and a plurality of relationships between the plurality of objects. Next, the processing system may label a first plurality of features of the feature graph database with at least a first community label, where the first plurality of features comprises features of at least a first community of the plurality of communities. The processing system may then obtain a search associated with at least one feature of the feature graph database, where the at least one feature is a part of the at least the first plurality of features of the at least the first community, and provide the first plurality of features in response to the search.

Abstract: A processing system may maintain a communication graph that includes nodes representing a plurality of phone numbers including a first phone number and edges between the nodes representing a plurality of communications between the plurality of phone numbers and may generate at least one vector via a graph embedding process applied to the communication graph, the at least one vector representing features of at least a portion of the communication graph. The processing system may then apply the at least one vector to a prediction model that is implemented by the processing system and that is configured to predict whether the first phone number is associated with a type of network activity associated with a telecommunication network and may implement a remedial action in response to an output of the prediction model indicating that the first phone number is associated with the type of network activity.

Abstract: A processing system may obtain a feature vector for a relationship between first and second user identities within a telecommunication network, the feature vector including: a first number of communications from the first user identity to the second user identity for a first communication channel, a first volume associated with the first number of communications, a second number of communications from the second user identity to the first user identity for the first communication channel, and a second volume associated with the second number of communications. The processing system may then calculate a scaled distance between the feature vector and a centroid comprising a mean vector of a set of relationships between user identities within the telecommunication network, where the scaled distance is associated to a trust value, and perform at least one remedial action in the telecommunication network based on the trust value.

Abstract: A processing system may maintain a communication graph that includes nodes representing a plurality of phone numbers including a first phone number and edges between the nodes representing a plurality of communications between the plurality of phone numbers and may generate at least one vector via a graph embedding process applied to the communication graph, the at least one vector representing features of at least a portion of the communication graph. The processing system may then apply the at least one vector to a prediction model that is implemented by the processing system and that is configured to predict whether the first phone number is associated with a type of network activity associated with a telecommunication network and may implement a remedial action in response to an output of the prediction model indicating that the first phone number is associated with the type of network activity.

Abstract: A processing system may maintain a relationship graph that includes nodes and edges representing phone numbers and device identifiers having associations with the phone numbers. The processing system may obtain an identification of a first phone number or a first device identifier for a fraud evaluation and extract features from the relationship graph associated with at least one of the first phone number or the first device identifier. The plurality of features may include one or more device identifiers associated with the first phone number, or one or more phone numbers associated with the first device identifier. The processing system may then apply the features to a prediction model that is implemented by the processing system and that is configured to output a fraud risk value of the first phone number or the first device identifier and implement at least one remedial action in response to the fraud risk value.

Abstract: Aspects of the subject disclosure may include, for example, obtaining a first indication of a user of a communication device browsing a first website associated with a first vendor, obtaining a second indication of the user of the communication device browsing a second website associated with a second vendor that is different from the first vendor, determining, in accordance with the first indication and the second indication, that the first vendor and the second vendor operate in a common marketplace, responsive to the determining, obtaining an advertisement of the first vendor to provide to the communication device, and providing the advertisement to the communication device. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, obtaining a first indication of a user of a communication device browsing a first website associated with a first vendor, obtaining a second indication of the user of the communication device browsing a second website associated with a second vendor that is different from the first vendor, determining, in accordance with the first indication and the second indication, that the first vendor and the second vendor operate in a common marketplace, responsive to the determining, obtaining an advertisement of the first vendor to provide to the communication device, and providing the advertisement to the communication device. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, processing a plurality of user profiles via a clustering algorithm to generate a plurality of clusters, computing a distance between a first cluster included in the plurality of clusters and at least one other cluster included in the plurality of clusters, identifying a first creative based on the distance, and providing the first creative to the first cluster. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, receiving, by a processing system including a processor, network access data for a first device of a first user and a second device of a second user, training a model based on the network access data to develop a first topological signature for the first device and a second topological signature for the second device, determining a relationship among the first user and the second user based on the first topological signature and the second topological signature, and providing network information such as advertising to the first device and to the second device based on the relationship among the first user and the second user. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, receiving viewership data for a plurality of devices associated with a household, the devices for viewing content items received over a network from a network provider at the household, concatenating respective viewership data for respective devices of the plurality of devices, forming respective device documents, vectorizing the respective device documents to form vectorized device documents, concatenating the vectorized device documents to form a household corpus of viewership data for the household, training a clustering model on the household corpus of viewership data to form a household topological fingerprint (HTF) for the household, the HTF forming a vector classification of viewership patterns for the household, and selecting content items for the household based on the HTF. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, obtaining a first indication of a user of a communication device browsing a first website associated with a first vendor, obtaining a second indication of the user of the communication device browsing a second website associated with a second vendor that is different from the first vendor, determining, in accordance with the first indication and the second indication, that the first vendor and the second vendor operate in a common marketplace, responsive to the determining, obtaining an advertisement of the first vendor to provide to the communication device, and providing the advertisement to the communication device. Other embodiments are disclosed.