Graph Node Aggregation for Estimation Accuracy
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Solution Overview
Problem
Graph analysis tasks that involve estimating unknown node information in large graphs with many nodes require significant computational effort, leading to long processing times due to the complexity of huge connected subgraphs.
Innovation Solution
A method that simplifies the graph by aggregating nodes with similar information into a single node and transforming multiple edges between them into a single edge with an aggregate weight, reducing the number of nodes and edges in the graph, thereby reducing computational effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If graph analysis is performed on huge connected subgraphs with many nodes, then estimation accuracy is maintained, but computational effort increases and processing time becomes long
Solution Approach 1:
The patent aggregates multiple nodes with similar node information into a single aggregate node, and merges multiple inter-node edges connecting to the same target node into a single aggregate edge with combined weight. This reduces the graph size from O(N) nodes to O(M) nodes where M < N, thereby decreasing computational complexity while preserving estimation accuracy through weighted aggregation that maintains the collective influence of original nodes
2Productivity
If the number of nodes and edges in the graph is reduced by aggregation, then computational effort decreases, but there is a risk of losing estimation precision
Solution Approach 1:
The patent transforms the graph structure by changing the parameters of nodes and edges through aggregation. Aggregate nodes represent groups of original nodes, and aggregate edges have weights that are sums of individual edge weights. This parameter transformation maintains the essential relationships and information flow in the graph while reducing complexity, thereby preserving estimation precision despite reduced graph size
Data Source
AI summary
A memory stores graph information representing a graph that includes nodes and inter-node edges. The nodes include a first plurality of nodes each associated with node information and a first node. Each of the inter-node edges has a weight. A processor extracts, in accordance with the node information, two or more nodes and transforms the two or more nodes into an aggregate node. The processor generates an aggregate inter-node edge between the aggregate node and the first node. The aggregate inter-node edge is associated with a weight based on two or more weights associated with two or more inter-node edges between the two or more nodes and the first node. The processor estimates first node information to be associated with the first node based on transformed graph information representing a transformed graph including the aggregate node and the aggregate inter-node edge.