Graph Querying via Closure Tree Index and Random Walk
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Solution Overview
Problem
Current methods for querying and mining graph databases are inefficient due to the computational difficulty of subgraph isomorphism and graph similarity, especially in large datasets, which limits the ability to analyze and understand complex networks like protein interaction networks effectively.
Innovation Solution
A method using a closure tree index structure for topological summarization of graphs, combined with a significance model and random walk techniques to detect connected substructures and assign statistical significance to patterns, facilitating both subgraph and similarity queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional graph querying methods are used, then query accuracy is maintained, but query efficiency deteriorates due to computational difficulty of subgraph isomorphism
Solution Approach 1:
The patent segments the graph database into multiple partitions or blocks, allowing queries to be processed in parallel across partitions. This divides the computationally intensive subgraph isomorphism problem into smaller, manageable sub-problems that can be solved independently and concurrently, thereby improving query efficiency while maintaining accuracy.
Solution Approach 2:
The patent employs preprocessing techniques to compute and store graph invariants, canonical labels, and other structural features before queries are executed. This preliminary action enables faster query processing by avoiding redundant computations during actual querying, thus improving productivity without sacrificing query accuracy.
2Measurement precision
If exhaustive graph comparisons are performed, then pattern detection accuracy is improved, but processing time increases significantly
Solution Approach 1:
The patent applies partial action by implementing early termination conditions in graph comparison algorithms. When certain structural properties or invariants are mismatched during comparison, the algorithm terminates early without completing the full isomorphism check. This reduces processing time while maintaining accuracy by ensuring that only potentially matching graphs undergo complete verification.
Solution Approach 2:
The patent introduces intermediary structures such as graph invariants, canonical forms, and indexing mechanisms that serve as mediators between the query graph and database graphs. These intermediaries enable rapid filtering of non-matching graphs before detailed comparison, thus reducing processing time while preserving pattern detection accuracy through subsequent verification steps.
3Loss of information
If detailed graph analysis is performed, then discovery of meaningful patterns is improved, but computational resources are consumed excessively
Solution Approach 1:
The patent applies local quality by focusing computational analysis on specific regions or substructures of graphs that are most likely to contain meaningful patterns. Instead of uniformly analyzing entire graphs, the method identifies and concentrates resources on locally significant areas such as densely connected subgraphs or regions matching query patterns, thereby improving pattern discovery quality while reducing overall computational resource consumption.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting analysis depth, granularity, and computational thresholds based on query characteristics and data properties. This allows the system to allocate computational resources efficiently by performing detailed analysis only where necessary and using coarser approximations elsewhere, thus maintaining pattern discovery quality while optimizing resource usage.
Data Source
AI summary
A method for analyzing, querying, and mining graph databases using subgraph and similarity querying. An index structure, known as a closure tree, is defined for topological summarization of a set of graphs. In addition, a significance model is created in which the graphs are transformed into histograms of primitive components. Finally, connected substructures or clusters, comprising paths or trees, are detected in networks found in the graph databases using a random walk technique and a repeated random walk technique.