Edge-Centric Embeddings of Digraphs: Properties and Stability Under Sparsification
- 1Department of Computer Science and Artificial Intelligence, University of Alicante, 03690 Alicante, Spain.
- 0Department of Computer Science and Artificial Intelligence, University of Alicante, 03690 Alicante, Spain.
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an edge-centric graph embedding approach, outperforming node-centric methods for classification and clustering. It leverages line digraphs and a linearity theorem for enhanced link mining and node representation.
Area Of Science
- Graph Theory and Network Analysis
- Machine Learning
- Data Mining
Background
- Traditional graph embedding methods primarily focus on node representations.
- Existing approaches often infer edge information indirectly from node similarities.
- There is a need for methods that directly capture edge and higher-order entity relationships in directed graphs (digraphs).
Purpose Of The Study
- To define and characterize edge and higher-order entity embeddings in digraphs.
- To develop an edge-centric approach that relates these embeddings to node embeddings.
- To improve performance in link mining, node classification, and clustering tasks.
Main Methods
- Embedding line digraphs and their iterated versions.
- Utilizing rank properties to express edge/path similarity as a linear combination of node similarities.
- Implementing digraph sparsification for scalability and evaluating performance using node2vec-like embeddings and Graph Neural Networks (GNNs).
Main Results
- The proposed edge-centric approach, based on embedding line digraphs, demonstrates superior performance over node-centric methods.
- The 'linearity theorem' is established, showing edge embedding transition matrices are linear combinations of node embedding matrices.
- Digraph sparsification proves effective for scalability, maintaining stable performance with increased sparsification levels.
Conclusions
- Edge-centric embeddings derived from line digraphs offer a powerful alternative for analyzing directed graphs.
- This method enhances link discovery, node classification, and clustering by directly modeling edge relationships.
- The approach is scalable and adaptable, showing promise for improving various graph-based machine learning tasks.
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