Abstract: Systems and methods for data visualization and network extraction in accordance with embodiments of the invention are illustrated. One embodiment includes a method including obtaining a graph comprising a plurality of nodes and a plurality of edges, identifying a plurality of communities in the graph, where each community includes nodes from the plurality of nodes, generating a community graph structure based on the identified communities, where the community graph includes a plurality of supernodes and a plurality of superedges, spatializing the community graph structure, unpacking the spatialized community graph structure into an unpacked graph structure comprising the plurality of nodes and the plurality of edges, where each node in the plurality of nodes is located at approximately the position of the supernode that represented it, spatializing the unpacked graph structure, and providing the spatialized unpacked graph structure.

Abstract: Systems and methods for data visualization and network extraction in accordance with embodiments of the invention are illustrated. One embodiment includes a method including obtaining a graph comprising a plurality of nodes and a plurality of edges, identifying a plurality of communities in the graph, where each community includes nodes from the plurality of nodes, generating a community graph structure based on the identified communities, where the community graph includes a plurality of supernodes and a plurality of superedges, spatializing the community graph structure, unpacking the spatialized community graph structure into an unpacked graph structure comprising the plurality of nodes and the plurality of edges, where each node in the plurality of nodes is located at approximately the position of the supernode that represented it, spatializing the unpacked graph structure, and providing the spatialized unpacked graph structure.

Abstract: Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes.

Abstract: Systems and methods for data visualization and network extraction in accordance with embodiments of the invention are illustrated. One embodiment includes a method including obtaining a graph comprising a plurality of nodes and a plurality of edges, identifying a plurality of communities in the graph, where each community includes nodes from the plurality of nodes, generating a community graph structure based on the identified communities, where the community graph includes a plurality of supernodes and a plurality of superedges, spatializing the community graph structure, unpacking the spatialized community graph structure into an unpacked graph structure comprising the plurality of nodes and the plurality of edges, where each node in the plurality of nodes is located at approximately the position of the supernode that represented it, spatializing the unpacked graph structure, and providing the spatialized unpacked graph structure.

Abstract: Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes.

Abstract: Systems and methods for dataset merging using flow structures in accordance with embodiments of the invention are illustrated. Flow structures can be generated and sent to various computing devices to generate both the front-end and back-end of a customized computing system that can perform any number of various processes including those that merge datasets. In many embodiments, machine learning and/or natural language processing can be performed by the customized application.

Abstract: Systems and methods for data visualization and network extraction in accordance with embodiments of the invention are illustrated. One embodiment includes a method including obtaining a graph comprising a plurality of nodes and a plurality of edges, identifying a plurality of communities in the graph, where each community includes nodes from the plurality of nodes, generating a community graph structure based on the identified communities, where the community graph includes a plurality of supernodes and a plurality of superedges, spatializing the community graph structure, unpacking the spatialized community graph structure into an unpacked graph structure comprising the plurality of nodes and the plurality of edges, where each node in the plurality of nodes is located at approximately the position of the supernode that represented it, spatializing the unpacked graph structure, and providing the spatialized unpacked graph structure.

Abstract: Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes.

Abstract: Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes.

Abstract: Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes.

Abstract: Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes.

Abstract: Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes.