Graph Representation

Technique

Graph representation is a technique that encodes entities (as vertices/nodes) and the relationships between them (as edges) so that problems can be analyzed using graph algorithms such as subgraph isomorphism, connected-component analysis, and graph similarity. It is used in domains ranging from specialized processor extension design and circuit diagram retrieval to large-scale graph representation learning.

First seen 6/9/2026

Last seen 6/9/2026

Evidence 1 chunks

Wiki v1

WIKI

Overview

Graph representation encodes a problem domain as a set of vertices (nodes) and edges, enabling the use of graph algorithms to reason about structure, connectivity, and substructure. The technique underpins methods in reconfigurable processor design, machine learning over graph-structured data, and retrieval over specialized diagram domains.

Use in Reconfigurable Processor Extension Design

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NEIGHBORHOOD

3 nodes · 3 edges

graph · Graph Representation · depth=1

RELATIONSHIPS

2 connections

IFPEC ← uses 100% 1e

IFPEC uses graph representation for both custom instructions and applications.

Constraint Programming Approach to Reconfigurable Processor Extension Generation and Application Compilation ← uses 100% 1e

The paper uses graph representation for both custom instructions and application modeling.

LINKED ENTITIES

2 links

IFPEC USES Extracted graph relationship

Constraint Programming Approach to Reconfigurable Processor Extension Generation and Application Compilation USES Extracted graph relationship

CITATIONS

3 sources

3 citations — click to collapse

[1] In the IFPEC framework, custom instructions and applications are represented as graphs, and the CP framework uses subgraph isomorphism and connected component constraints to identify, select, schedule, bind, and route processor extensions on architectures composed of runtime reconfigurable cells tightly connected to a processor. Constraint Programming Approach to Reconfigurable Processor Extension Generation and Application Compilation

[2] Graph representation learning requires large, diverse graph databases; MalNet provides over 1.2 million graphs averaging more than 15k nodes and 35k edges, organized into 47 types and 696 families, and is roughly 105x larger in graph count, 39x larger in average size, and 63x more diverse in classes than REDDIT-12K. A Large-Scale Database for Graph Representation Learning

[3] Graph representation of analog circuit diagrams, combined with a GAM-YOLO-based recognition algorithm, 2-step connected-domain filtering, and a hierarchical graph-similarity retrieval strategy, reformulates circuit diagram retrieval as a graph retrieval problem that exploits topological structure ignored by standard image retrieval methods. Circuit Diagram Retrieval Based on Hierarchical Circuit Graph Representation