Abstract: Techniques are described herein for address matching from a single address string to an address matching score. In an embodiment, an address string is received and parsed into parsed address data. Once an address string is parsed into parsed address data, the parsed address data is standardized by converting the parsed address data into a standard format and replacing abbreviations, colloquial names with formal names. Once an address string has been standardized into a standardized street locale, candidate addresses that are identical to or similar to the standardized street locale are identified and are assigned a score. Each score comprises a probability that the respective candidate address and the standardized street locale represent a same place or location.

Abstract: Herein is machine learning for anomalous graph detection based on graph embedding, shuffling, comparison, and unsupervised training techniques that can characterize an unfamiliar graph. In an embodiment, a computer obtains many known vectors that respectively represent known graphs. A new vector is generated that represents a new graph that contains multiple vertices. The new vector may contain an arithmetic aggregation of vertex vectors that respectively represent multiple vertices and/or a vector that represents a virtual vertex that is connected to the multiple vertices by respective virtual edges. In the many known vectors, some similar vectors that are similar to the new vector are identified. The new graph is automatically characterized based on a subset of the known graphs that the similar vectors represent.

Abstract: In an embodiment, a computer stores a bipartite graph that consists of a source subgraph and a target subgraph. Each vertex in the bipartite graph represents an entity. The source subgraph and the target subgraph are connected by many similarity edges. Each similarity edge indicates an original amount of similarity between the entity of a source vertex in the source subgraph and the entity of a target vertex in the target subgraph. For each similarity edge, the computer determines: a set of neighbor source vertices that are reachable from the source vertex of the similarity edge by traversing at most a source radius count of source edges in the source subgraph, a set of neighbor target vertices that are reachable from the target vertex of the similarity edge by traversing at most a target radius count of target edges in the target subgraph, and various amounts based on graph topology. For each similarity edge, the computer calculates a new amount of similarity based on those various amounts.

Abstract: Techniques are described herein for a Name Matching Engine that integrates two Machine Learning (ML) module options. The first ML module is a feature-engineered classifier that boosts text-based name matching techniques with a binary classifier ML model. The feature-engineered classifier comprises a first stage of text-based candidate finding, and a second stage in which a binary classifier model predicts whether each string, of the candidate match list, is a match or not. The binary classifier model is based on features from two or more of: a name feature level, a word feature level, a character feature level, and an initial feature level. The second ML module of the Name Matching Engine comprises an end-to-end Recurrent Neural Network (45RNN) model that directly accepts name strings as a sequence of n-grams and generates learned text embeddings. The text embeddings of matching name strings are close to each other in the feature space.