Determining more accurate labels for nodes in a graph

A method for determining more accurate labels for nodes in a graph, the method comprising:

• training, with an electronic processor, a first machine learning model and a second machine learning model, wherein the first machine learning model and the second machine learning model are initialized with different model weights;
• receiving, with the electronic processor, a graph including a plurality of nodes linked by one or more connections, wherein
• each node of the plurality of nodes represents a card with a chargeback claim or represents a merchant,
• each node of the plurality of nodes is unlabeled or associated with a plurality of potential labels, and
• a connection between a first node and a second node represents a transaction made between a merchant and a user using a card with a chargeback claim;
• augmenting the graph by creating one or more new connections in the graph;
• for each node of the plurality of nodes included in the augmented graph:
• using the first machine learning model, determining a first vector associated with the node based on the augmented graph, wherein
• each value included in the first vector is associated with a label and represents a likelihood that the label is a more accurate label,
• when the node represents a card with a chargeback claim, each label associated with a value included in the first vector is third party fraud, first party fraud, or technical error, and
• when the node represents a merchant, each label associated with a value included in the first vector is a merchant category code;
• using the second machine learning model, determining a second vector associated with the node based on the augmented graph, wherein
• each value included in the second vector is associated with a label and represents a likelihood that the label is the more accurate label,
• when the node represents a card with a chargeback claim, each label associated with a value included in the second vector is third party fraud, first party fraud, or technical error, and
• when the node represents a merchant, each label associated with a value included in the second vector is a merchant category code;
• and
• determining the more accurate label for the node based on the first vector and the second vector;
• and
• sending, with the electronic processor, a determination of whether to allow or deny a transaction based on one or more more accurately labeled nodes to a server, wherein the server is configured to perform or deny the transaction based on the determination,
• wherein training the first machine learning model and the second machine learning model further includes determining a consistency regulation loss value based on the first vector and the second vector.

A non-transitory computer-readable medium comprising executable instructions that, when executed by an electronic processor, cause the electronic processor to perform a set of functions comprising:

• training a first machine learning model and a second machine learning model, wherein the first machine learning model and the second machine learning model are initialized with different model weights;
• receiving a graph including a plurality of nodes linked by one or more connections, wherein
• each node of the plurality of nodes represents a card with a chargeback claim or represents a merchant,
• each node of the plurality of nodes is unlabeled or associated with a plurality of potential labels, and
• a connection between a first node and a second node represents a transaction made between a merchant and a user using a card with a chargeback claim;
• augmenting the graph by creating one or more new connections in the graph;
• for each node of the plurality of nodes included in the augmented graph:
• using the first machine learning model, determining a first vector associated with the node based on the augmented graph, wherein
• each value included in the first vector is associated with a label and represents a likelihood that the label is a more accurate label,
• when the node represents a card with a chargeback claim, each label associated with a value included in the first vector is third party fraud, first party fraud, or technical error, and
• when the node represents a merchant, each label associated with a value included in the first vector is a merchant category code;
• using the second machine learning model, determining a second vector associated with the node based on the augmented graph, wherein
• each value included in the second vector is associated with a label and represents a likelihood that the label is the more accurate label,
• when the node represents a card with a chargeback claim, each label associated with a value included in the second vector is third party fraud, first party fraud, or technical error, and
• when the node represents a merchant, each label associated with a value included in the second vector is a merchant category code;
• and
• determining the more accurate label for the node based on the first vector and the second vector;
• and
• sending, to a server, a determination of whether to allow or deny a transaction based on one or more more accurately labeled nodes, wherein the server is configured to perform or deny the transaction based on the determination,
• wherein training the first machine learning model and the second machine learning model further includes determining a consistency regulation loss value based on the first vector and the second vector.