Fuji Xerox Co., Ltd.

Electronic Arts Inc.

Bokai Cao

Francine R Chen

Dhiraj Joshi

Yin-Ying Chen

Siim Viidu

Dhjraj Joshi

Ming Mao

Heterogeneous information networks can be used to detect whether a new transaction is fraudulent. Metapaths are determined based on the heterogeneous information. The transactions can be downsized. For each metapath, training transaction data and test transaction data be used to compute a feature vector 'z.' A fraud detection system can use the features to generate a fraud detection model. The fraud detection model can be iteratively refined using machine learning. The fraud detection models can then be applied to new transactions to determine a risk score of the new transaction.

Fraud detection in heterogeneous information networks

A method for inferring venues from social messages includes: accessing a collection of venues and training a classifier that predicts whether a social message is linked to a venue in the collection of venues; receiving a new social message; for each venue in the collection of venues: identifying for the new social message corresponding meta-paths to the particular venue; encoding the corresponding meta-paths as a feature vector for the trained classifier; computing by the trained classifier a score for each venue in the collection of venues indicating whether the new social message is linked to the venue; and based on the scores, identifying at least one candidate venue as a predicted venue for the new social message and associating the predicted venue with the new social message. In some implementations, the new social message is not geotagged.

Methods and systems of venue inference for social messages

A method associates social media messages with venues. A social network graph includes users, messages from users, and venues. The venues include multiple primary venues and a no-venue. A link between a message and the no-venue node indicates that the message is not associated with a primary venue. Training feature vectors are constructed that measure connectedness between messages and venues. The process trains a classifier to estimate probabilities that messages are associated with venues. A new social media message is received, and the process constructs a feature vector using the same features as the training vectors, measuring connectedness between the new message and the no-venue. The classifier computes a probability that the new message is associated with the no-venue. When the probability exceeds a predefined threshold, the new message is not associated with any of the primary venues. Otherwise, the new message is associated with one of the primary venues.

Venue link detection for social media messages

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Chicago, IL, United States of America

Bokai Cao