Social Graph User Characteristic Scoring via Affinity Propagation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Social networking systems face challenges in identifying users with specific characteristics, such as positive or negative attitudes towards concepts, due to limited information and the need to determine additional users who likely possess these characteristics to enhance user experience and content recommendation.
Innovation Solution
The method involves using machine-learning algorithms to identify a set of user nodes with specific characteristics in a social graph, assigning scores to these nodes, and then estimating scores for other nodes based on their connections, allowing for the inference of users likely to have these characteristics by traversing the social graph through high-affinity neighboring nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learning algorithms are used to identify users with specific characteristics, then measurement precision of user characteristics is improved, but device complexity increases
Solution Approach 1:
The patent uses affinity scores as an intermediary mechanism to bridge the gap between limited user information and accurate characteristic identification. By introducing this intermediate scoring system that quantifies relationships between users and concepts, the system achieves precise user characteristic measurement without requiring complex direct analysis of user behavior patterns.
Solution Approach 2:
The system implements feedback loops where affinity scores are continuously updated based on user interactions with content and concepts. This feedback mechanism allows the machine-learning algorithms to iteratively improve their accuracy in identifying user characteristics, resolving the contradiction by making the complexity productive through continuous refinement rather than static complex processing.
2Quantity of substance
If the system traverses the social graph to estimate scores for additional users, then quantity of identified users is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing affinity scores for user-concept relationships and user-user connections in the social graph. When needing to identify users with specific characteristics, the system leverages these pre-computed scores rather than performing full graph traversals, thus expanding the quantity of identifiable users while minimizing time loss through efficient score estimation based on existing data.
Solution Approach 2:
Instead of traversing the entire social graph to identify all users with specific characteristics, the system performs partial action by focusing traversal on relevant portions of the graph starting from seed users. This selective approach identifies sufficient quantity of target users without the excessive time cost of exhaustive graph traversal, applying the principle of doing enough rather than everything.
3Adaptability or versatility
If the system uses connection-based score estimation, then adaptability of user characteristic identification is improved, but measurement precision may deteriorate due to indirect inference
Solution Approach 1:
The patent applies local quality by weighting affinity scores differently based on the local context of connections in the social graph. Rather than treating all connections uniformly, the system assigns different weights to direct connections versus indirect connections, and to connections with different affinity strengths. This localized weighting preserves measurement precision for each user by considering their specific connection quality, while maintaining adaptability across diverse user groups through the generalizable weighting scheme.
Data Source
AI summary
In one embodiment, one or more computing devices access a social graph including nodes and edges between nodes; the nodes include user nodes corresponding to users of a social-networking system and concept nodes corresponding to concepts of the social-networking system; the computing devices identify first user nodes in the social graph corresponding to users having a particular user characteristic; the computing devices assign a characteristic score to each first user node; for a second user node without an assigned characteristic score, the computing devices also select neighboring user nodes connecting to the second user node based on affinity, and estimate a characteristic score for the second user node by aggregating iteratively characteristic scores while traversing the social graph from at least one first user node to the second user node through paths that each includes at least one selected neighboring node.