Graph Search Engine Structural Property Matching
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Solution Overview
Problem
Conventional search engines are inefficient in searching graph data with limited textual information, as they fail to utilize the structural properties and characteristics of graphs, leading to poor performance in data mining and digital content retrieval.
Innovation Solution
A graph search engine system that takes a user-inputted query graph and returns a ranked list of relevant graphs based on structural properties, metadata, and user-defined constraints, using techniques such as representation learning, normalization, and online learning to enhance search efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional search engines use textual information for searching, then they can effectively search websites and documents with large amounts of textual information, but they work poorly for graph data with limited textual information and cannot utilize structural properties
Solution Approach 1:
The patent transforms the search approach by changing the fundamental parameters from text-based matching to graph structure-based matching. It computes structural properties (degree, clustering coefficient, betweenness centrality, etc.) as new parameters for graph representation, enabling the search engine to effectively query graph data by comparing structural characteristics rather than relying on limited textual metadata.
2Ease of operation
If conventional search engines rely on textual content for matching, then they can return lists of potential matches based on text, but they cannot answer simple graph queries based on structural properties
Solution Approach 1:
The patent replaces the mechanical text-matching mechanism with a computational graph analysis mechanism. Instead of comparing textual strings, the system computes structural properties of graphs (such as degree distribution, clustering coefficients, and centrality measures) and uses these computed metrics for matching, thereby enabling precise structural property queries while maintaining user-friendly operation.
3Productivity
If conventional search engines process vast amounts of digital content using text-based methods, then they can handle web sites and documents, but they result in an inefficient system for data mining and searching graph data
Solution Approach 1:
The patent implements preliminary action by pre-computing and storing structural properties of graphs in advance. The system calculates key structural metrics (degree, clustering coefficient, betweenness centrality, etc.) beforehand and stores them in an optimized format, so that when a search query arrives, the system can quickly retrieve and compare pre-computed properties rather than calculating them from scratch, significantly improving search efficiency for graph data.
Data Source
AI summary
One embodiment provides a system for facilitating a graph search engine. During operation, the system receives, by a server from a client computing device, a search request which includes a user-inputted graph. The system performs a search based on a structure of the user-inputted graph for a plurality of relevant graphs. The system orders the plurality of relevant graphs from a most relevant ranking to a least relevant ranking. The system returns, to the client computing device, the ordered plurality of relevant graphs for display on a user interface of the client computing device, thereby enhancing the search for relevant graphs by allowing the graph search engine to take as an input the user-inputted graph and return as an output the relevant graphs.