Abstract: Methods, systems, and devices for analyzing communication messages (e.g., emails or activities) to determine relationship strength using a distributed graph are described. In some systems, a user may be associated with a specific tenant. A database server of the system may receive communication messages associated with the user and a target user. The server may perform a natural language processing (NLP) analysis on the communication messages to extract metadata, and may generate or update a distributed graph indicating connections between users based on the extracted metadata. Using the connections of the graph, the server may calculate a closeness score between the user and the target user. Additionally, the server may calculate closeness scores between the target and other users associated with the tenant, and may determine the users with the greatest closeness scores. The server may send a suggestion for the determined users to initiate communication with the target.

Abstract: A database server may identify mentioned names in a body of a message and extract the names using name identification heuristics and algorithms. The service retrieves or utilizes a distributed connection graph to identify contacts associated with the parties to the conversation that may match or be similar to the mentioned name. Contacts may be scored based on similarities between the extracted name the names associated with nodes of the graph, as well as other factors. The highest scoring contact may be surfaced or displayed to one or more of the parties to the communication message.

Abstract: A database server may analyze interaction data including communication to generate a graph representation of various users and connections between the users. The database server may utilize the graph representation of connections to identify sufficiently connected target user identifiers in one or more external organizations. A connection metric may be assigned to each user identifier of one or more groups of user identifiers generated using the graph representation, and the target user identifiers may be identified based on the connection metrics.

Abstract: A database server may identify mentioned names in a body of a message and extract the names using name identification heuristics and algorithms. The service retrieves or utilizes a distributed connection graph to identify contacts associated with the parties to the conversation that may match or be similar to the mentioned name. Contacts may be scored based on similarities between the extracted name the names associated with nodes of the graph, as well as other factors. The highest scoring contact may be surfaced or displayed to one or more of the parties to the communication message.

Abstract: A database server may analyze interaction data including communication to generate a graph representation of various users and connections between the users. The database server may utilize the graph representation of connections to identify sufficiently connected target user identifiers in one or more external organizations. A connection metric may be assigned to each user identifier of one or more groups of user identifiers generated using the graph representation, and the target user identifiers may be identified based on the connection metrics.

Abstract: Methods, systems, and devices for analyzing communication messages (e.g., emails or activities) to determine relationship strength using a distributed graph are described. In some systems, a user may be associated with a specific tenant. A database server of the system may receive communication messages associated with the user and a target user. The server may perform a natural language processing (NLP) analysis on the communication messages to extract metadata, and may generate or update a distributed graph indicating connections between users based on the extracted metadata. Using the connections of the graph, the server may calculate a closeness score between the user and the target user. Additionally, the server may calculate closeness scores between the target and other users associated with the tenant, and may determine the users with the greatest closeness scores. The server may send a suggestion for the determined users to initiate communication with the target.