Feature-Based Network Embedding for Temporal Graphs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing approaches for determining network embeddings from graph-based representations ignore node features, leading to inaccurate embeddings and poor performance in applications like user profiling and content customization.
Innovation Solution
A network analysis system generates network embeddings by traversing a temporal graph, replacing node IDs with feature values such as indegree, outdegree, and total degree, and using these feature values to create edge embeddings, which are then used to improve online platform modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If node IDs are used to represent nodes in graph-based network embedding approaches, then the analysis process is simple and straightforward, but the accuracy of network embeddings deteriorates because node features are ignored
Solution Approach 1:
The patent transforms node representations from simple IDs to feature vectors by changing the parameters used to describe nodes. Instead of using only node identifiers, the system incorporates multiple node features (indegree, outdegree, total degree) as parameters in the embedding process, thereby improving measurement precision while managing complexity through systematic feature integration
Solution Approach 2:
The patent segments the node representation into multiple feature components (indegree feature, outdegree feature, total degree feature) rather than treating nodes as monolithic IDs. This segmentation allows each feature to contribute independently to the embedding accuracy, resolving the contradiction by making the analysis process more structured and manageable despite increased complexity
2Reliability
If node features such as indegree and outdegree are incorporated into network embedding analysis, then the accuracy of embeddings improves, but the complexity of the analysis process increases
Solution Approach 1:
The patent creates a universal feature-based embedding framework that can handle multiple node features (indegree, outdegree, total degree) through a unified approach. This multi-functionality allows the system to incorporate various features without proportionally increasing complexity, as the same embedding methodology applies to each feature type, thereby improving reliability while controlling complexity
3Productivity
If existing graph-based approaches are used that ignore node features, then the analysis is computationally efficient, but the resulting embeddings are inaccurate leading to poor application performance
Solution Approach 1:
The patent applies partial action by selectively incorporating the most important node features (indegree, outdegree, total degree) rather than processing all possible features. This selective approach maintains computational efficiency while significantly improving embedding accuracy, resolving the contradiction between productivity and measurement precision
Data Source
AI summary
A system receives a temporal graph comprising nodes having respective identifiers and edges. Each of the edges has a direction pointing from a first node to a second node and indicates an association of the first node with the second node. The system generates a sequence of nodes and a sequence of edges by traversing the temporal graph. The system determines, for each node of the sequence of nodes, a respective set of feature values including an indegree, an outdegree, and a total degree. The system determines, for each edge of the sequence of edges, an edge feature comprising a sum of the total degree of a first node preceding the edge and the total degree of a second node following the edge. The system forms an edge feature values sequence for the sequence of edges and determines an edge network embedding for each edge of the sequence of edges.