Typed Graphlet Clustering for Heterogeneous Network Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional graph analysis methods are inadequate for handling heterogeneous graphs, where nodes and edges have different types, as they fail to preserve type information and accurately model higher-order connectivity patterns, leading to incorrect clustering of dissimilar nodes as similar.
Innovation Solution
The implementation of a network clustering module that uses typed graphlets to derive a typed graphlet adjacency matrix and Laplacian matrix, allowing for accurate clustering and embedding of nodes in heterogeneous graphs by preserving node and edge type information, and leveraging eigenvectors to sort nodes and identify clusters based on typed graphlet conductance scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional graph analysis methods are used on heterogeneous graphs, then the analysis can be performed on large-scale graph data, but the type information of nodes and edges is lost and dissimilar nodes are incorrectly clustered as similar
Solution Approach 1:
The patent applies local quality by making different parts of the graph (nodes and edges) have different properties based on their types. Each node and edge is assigned type-specific weights and characteristics in the clustering algorithm, allowing the analysis to preserve and utilize type information locally throughout the heterogeneous graph structure.
Solution Approach 2:
The patent changes parameters by introducing type-specific weighting parameters for nodes and edges. The clustering algorithm uses these parameter changes to differentiate between various types of nodes and edges, transforming the conventional unweighted approach into a weighted approach that preserves type information while maintaining scalability.
2Device complexity
If conventional approaches model all nodes and edges as a single type, then the analysis is simpler and faster, but the type structure information is disregarded leading to incorrect clustering
Solution Approach 1:
The patent implements local quality by assigning different weights and properties to nodes and edges based on their specific types. This allows the algorithm to handle type-specific characteristics locally without requiring complete restructuring of the overall algorithm, thus managing complexity while improving accuracy.
Solution Approach 2:
The patent achieves universality by creating a generalized clustering framework that can handle both homogeneous and heterogeneous graphs. The type-aware clustering algorithm serves multiple functions: it works on simple graphs when type information is uniform and on complex heterogeneous graphs when type information varies, making the solution adaptable across different graph types.
3Loss of information
If conventional approaches consider higher-order connectivity patterns without type structure, then some clustering information is captured, but variations in type connectivity patterns are not identified
Solution Approach 1:
The patent applies parameter changes by introducing type-specific weighting parameters that modify how connectivity patterns are evaluated. The algorithm changes the parameters associated with each node and edge type, allowing higher-order connectivity patterns to be analyzed with type-aware discrimination, thus preventing loss of type-specific connectivity information.
Data Source
AI summary
In implementations of higher-order graph clustering and embedding, a computing device receives a heterogeneous graph representing a network. The heterogeneous graph includes nodes that each represent a network entity and edges that each represent an association between two of the nodes in the heterogeneous graph. To preserve node-type and edge-type information, a typed graphlet is implemented to capture a connectivity pattern and the types of the nodes and edges. The computing device determines a frequency of the typed graphlet in the graph and derives a weighted typed graphlet matrix to sort graph nodes. Sorted nodes are subsequently analyzed to identify node clusters having a minimum typed graphlet conductance score. The computing device is further implemented to determine a higher-order network embedding for each of the nodes in the graph using the typed graphlet matrix, which can then be concatenated into a matrix representation of the network.