Stripe, Inc.

Pranav Dandekar

Ashish Goel

Peter Lofgren

Matthew David Fisher

Andrew Paul Tausz

Described herein are systems and methods for predicting a metric value for an entity associated with a query node in a graph that represents a network. In embodiments, using a user's profile as the query node, a metric about that user may be estimated based, at least in part, as a function of how well connected the query node is to a whitelist of 'good' users/nodes in the network, a blacklist of “bad” users/nodes in the network, or both. In embodiments, one or more nodes or edges may be weighted when determining a final score for the query node. In embodiments, the final score regarding the metric may be used to take one or more actions relative to the query node, including accepting it into a network, allowing or rejecting a transaction, assigning a classification to the node, using the final score to compute another estimate for a node, etc.

Using one or more networks to assess one or more metrics about an entity

Aspects of the current disclosure include systems and methods for identifying an entity in a query image by comparing the query image with digital images in a database. In one or more embodiments, a query feature may be extracted from the query image and a set of candidate features may be extracted from a set of images in the database. In one or more embodiments, the distances between the query feature and the candidate features are calculated. A feature, which includes a set of shortest distances among the calculated distances and a distribution of the set of shortest distances, may be generated. In one or more embodiments, the feature is input to a trained model to determine whether the entity in the query image is the same entity associated with one of the set of shortest distances.

Entity recognition from an image

Described herein are systems and methods for predicting whether an entity associated with a profile in one network is the same entity that is associated with a profile in a second network, which networks may represent networks from different network services or may represent networks from the same network service. In embodiments, network graph features, including nodes and connections, may be used to predict a probability that the profiles in the two networks should be matched. In embodiments, additional or different factors may be included in the predicted probability, such as homophily, match probabilities of seed nodes, match probabilities of attribute-matched nodes, attribute-attribute co-occurrence probabilities, and the like.

Systems and methods for identifying entities between networks

Described herein are systems and methods for predicting a metric value for an entity associated with a query node in a graph that represents a network. In embodiments, using a user's profile as the query node, a metric about that user may be estimated based, at least in part, as a function of how well connected the query node is to a whitelist of 'good' users/nodes in the network, a blacklist of 'bad' users/nodes in the network, or both. In embodiments, one or more nodes or edges may be weighted when determining a final score for the query node. In embodiments, the final score regarding the metric may be used to take one or more actions relative to the query node, including accepting it into a network, allowing or rejecting a transaction, assigning a classification to the node, using the final score to compute another estimate for a node, etc.

Embodiments of the present disclosure include systems and methods for identifying people in an image that contains more than one images of people. In embodiments, a query feature representation that represents features is extracted from each image of a person. In embodiments, each query feature representation is compared to image feature representations in a database and a set of candidate representations is selected among the image feature representations. Then, a set of user accounts that is associated with the set of candidate representations is selected. The strengths of connection in a network between user accounts in a set of candidate user accounts corresponding to an image and user accounts in a different set of candidate user accounts corresponding to a different image may be determined. In embodiments, user accounts that has the highest strength of connection are selected and used to identify the persons corresponding to the images.

Determining identity in an image that has multiple people

Embodiments of the present disclosure include systems and methods for identifying people in an image that contains more than one face images. In embodiments, a query feature vector that represents features is extracted from each face image. In embodiments, each query feature vector is compared to image feature vectors in a database and a set of candidate vectors is selected among the image feature vectors. Then, a set of user accounts that is associated with the set of candidate vectors is selected. The strengths of connection in a network between user accounts in a set of candidate user accounts corresponding to a face image and user accounts in a different set of candidate user accounts corresponding to a different face image may be determined. In embodiments, user accounts that has the highest strength of connection are selected and used to identify the persons corresponding to the face images.

Determining identities of multiple people in a digital image

Systems and methods for using one or more networks to assess a metric about an entity

Aspects of the current disclosure include systems and methods for identifying a person in a query image by comparing the query image with digital images in a face database. In embodiments, a query feature vector may be extracted from the query image and a set of candidate feature vectors may be extracted from a set of images in the face database. In embodiments, the distances between the query feature vector and the candidate feature vectors are calculated. A feature vector, which includes a set of shortest distances among the calculated distances and a distribution of the set of shortest distances, may be generated. In embodiments, the feature vector is input to a trained decision tree to determine whether the person in the query image is the same person associated with one of the set of shortest distances.

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Palo Alto, CA, United States of America

Pranav Dandekar