Citigroup Technology, Inc.

Steve Thomas Dayton

Miriam Silver

William George Cormier

Paola Gittino

Methods and systems are described herein for identifying matching parameters of groups of nodes in graphical representations. The system may generate, in a graphical user interface, a graphical representation of nodes representing users associated with an entity. The system may activate the graphical representation as links connecting pairs of nodes, with the links representing interactions between users. The system may identify a grouping of nodes having a level of local clustering indicative of undesired activity. The system may determine graphical parameters relating to the level of local clustering and may identify the same graphical parameters in other groupings of nodes in other graphical representations. The system may thus identify indications of undesired activity based on matching parameters of groups of nodes.

Detecting undesirable activity based on matching parameters of groups of nodes in graphical representations

Methods and systems for network analysis, modeling, and visualization of electronic data involve using a machine learning algorithm for generating, in a relationship visualization aspect of a graphical user interface, a visualization consisting at least in part of a plurality of icons, each icon representing an interacting entity, and activating a visualization consisting at least in part of an element disposed between each of a plurality of pairs of the plurality of icons representing interactions between the users. The machine learning algorithm identifies communities of users based on created models of each interacting user. The machine learning algorithm displays the visualization based on predictions of how the display will be acknowledged by a viewer. The machine learning algorithm makes predictions of how an interacting user and the community will evolve over time.

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Brandon, FL, United States of America

Steve Thomas Dayton