Collaboration Graph Centrality for Targeted Communication Control
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Solution Overview
Problem
In computing systems hosting services for multiple organizations, controlling communication to specific users is challenging due to varying user interactions, often resulting in either overly inclusive or under-inclusive communication, leading to bandwidth inefficiencies and missed communications.
Innovation Solution
A system detects collaborative actions among users, generates a graph representing these interactions, calculates centrality metrics for each user based on their connections, and uses these metrics to control user interaction systems, ensuring targeted communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a communication system sends communications to a broad set of users to ensure coverage, then the reliability of delivery is improved, but the bandwidth consumption and computing overhead increase
Solution Approach 1:
The patent applies local quality by differentiating user importance through centrality metrics and sending communications selectively to high-centrality users rather than uniformly to all users. The system identifies key influencers within the collaboration graph and targets communications to those specific nodes, making the communication strategy non-uniform and adapted to local user importance characteristics.
Solution Approach 2:
The system changes the parameter of user selection from a static broad inclusion criterion to a dynamic centrality-based metric. By calculating connection metrics and centrality scores based on collaboration graph analysis, the system adapts the communication recipient selection parameter to reflect actual user importance and influence within the organization.
2Reliability
If a communication system sends communications to a broad set of users, then the coverage is improved, but the computing overhead increases
Solution Approach 1:
The system performs preliminary action by pre-calculating centrality metrics and building collaboration graphs before communications are sent. The graph construction and metric calculation are done in advance, allowing the communication system to quickly query pre-computed user importance scores rather than performing complex analysis at communication time, thus reducing real-time computing overhead.
Solution Approach 2:
The patent uses copying by creating a graph representation of user collaborations that mirrors the actual interaction patterns. This graph model serves as a simplified copy of the complex user relationship network, allowing the system to analyze and make decisions based on the graph structure rather than processing raw interaction data repeatedly.
3Loss of energy
If a communication system targets specific users to reduce bandwidth usage, then the bandwidth efficiency is improved, but the risk of missing intended recipients increases
Solution Approach 1:
The system implements feedback by using collaboration graph data to inform communication targeting decisions. The centrality metrics provide feedback about user importance and influence, allowing the system to adjust its communication strategy to target users who are most likely to receive and propagate communications effectively, thus maintaining reach while improving efficiency.
Solution Approach 2:
The patent replaces the mechanical approach of broadcasting to all users with an intelligent selection mechanism based on graph analysis. Instead of using a simple mechanical filter or random selection, the system substitutes a sophisticated algorithm that analyzes collaboration patterns and identifies optimal recipients based on their position and importance in the organizational graph.
4Ease of operation
If a communication system uses simple user lists for targeting, then the ease of operation is improved, but the precision of user identification deteriorates
Solution Approach 1:
The collaboration graph serves as an intermediary between simple user lists and precise user identification. The graph structure acts as a mediator that transforms basic user data into meaningful centrality metrics, providing an intermediate layer of analysis that bridges the gap between operational simplicity and identification precision.
Solution Approach 2:
The system adds another dimension to user identification by incorporating collaboration relationship data alongside basic user attributes. Instead of relying solely on one-dimensional user lists, the system multi-dimensionalizes the identification process by considering connection metrics, centrality scores, and graph position, thereby improving accuracy without significantly complicating operation.
Data Source
AI summary
Collaborative actions among users are detected and an indication of a graph, illustrating those collaborative actions, is generated. Nodes in the graph identify users and connections among nodes are indicative of collaborative actions between users. A connection metric, based upon a number of connections between nodes, is generated for each node, and an inheritance value is also generated for each given node, based upon a connection metric corresponding to other nodes connected to the given node. A centrality metric is generated for each node, and a control signal is generated, to control a user interaction system, based upon the centrality metric.