Kent State University

Graphsql, Inc.

Tigergraph, Inc.

Ruoming Jin

Yu Xu

Adam P Anthony

Ming Lin

Li Chen

Victor E Lee

Nicholas Tietz

Ning Ruan

Systems and methods for enhanced detection of fraudulent electronic transactions are disclosed. In one embodiment, a system uses the ongoing stream of transactions to construct and maintain a dynamically evolving merchant relationship graph. When a proposed transaction is submitted to the system, the system computes a predicted likelihood that the given account would make a transaction with these characteristics with the given merchant. The graph is used to compute transitive relatedness between merchants which may be indirectly associated with one another, as well as to compute aggregate relatedness, when there are multiple avenues of relationship between two merchants.

System and method for enhanced detection of fraudulent electronic transactions

Systems and methods for generating real-time, personalized recommendations are disclosed. In one embodiment, a method operates upon an electronic data collection organized as a network of vertices and edge connections between the vertices. The method provides the recommendations includes iteratively traversing across edges that satisfy search criteria to a new set of vertices and filtering each new set of vertices to satisfy the search criteria. At the conclusion of the traversing and filtering, a final set of vertices represents the recommended entities. In some embodiments, a control vector describes a sequence of relationships between a requester and the items to be recommended. The method can assign scores to candidate recommendations and select the recommendations having the highest scores. Advantageously, the method provides flexibility and rapid execution of recommendation queries without the need to precompute intermediate results.

System and method for real-time graph-based recommendations

Embodiments disclosed herein provide systems, methods, and software for determining the shortest distance between vertices in relatively large graphs. In an embodiment a method includes identifying an original graph comprising a plurality of nodes and a plurality of edges, identifying a plurality of hub nodes from within the original graph, creating a hub matrix comprising the plurality of hub nodes and the shortest distance between the plurality of hub nodes, and determining a shortest distance between the at least two nodes using the original graph and/or the hub matrix.

Shortest path computation in large networks

Methods and systems for distributed computation of graph data permit edge collection and vertex collection, each to be partitioned among a plurality of computational units. In one embodiment, the methods employ a two-phase computational cycle, which is repeated until the computation is complete. In a first phase, processing units process each active edge and vertex by doing the following: reading their current attribute values, executing programmed computational functions, updating edge attributes and sending data messages to vertices. In a second phase, each vertex update processor processes each of its active vertices by doing the following: reading its current attribute values and received data messages, executing a programmed computational function, and updating the vertex's attribute values.

Growing community of inventors

Kent, OH, United States of America

Ruoming Jin