mlsanityData to 7 Oct 2026

Instruction set for the representation of graphs

arXiv 2603.11039 ↗First posted 11 Mar 2026cs.CL cs.AI cs.DScited by 1

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Abstract (arXiv)

We present IsalGraph, a method for representing the structure of any finite, simple graph as a compact string over a nine-character instruction alphabet. The encoding is executed by a small virtual machine comprising a sparse graph, a circular doubly-linked list (CDLL) of graph-node references, and two traversal pointers. Instructions either move a pointer through the CDLL or insert a node or edge into the graph. A key design property is that every string over the alphabet decodes to a valid graph, with no invalid states reachable. A greedy GraphToString algorithm encodes any connected graph into a string in time polynomial in the number of nodes; an exhaustive-backtracking variant produces a canonical string by selecting the lexicographically smallest shortest string across all starting nodes and all valid traversal orders. We evaluate the representation on five real-world graph benchmark datasets (IAM Letter LOW/MED/HIGH, LINUX, and AIDS) and show that the Levenshtein distance between IsalGraph strings correlates strongly with graph edit distance (GED). Together, these properties make IsalGraph strings a compact, isomorphism-invariant, and language-model-compatible sequential encoding of graph structure, with direct applications in graph similarity search, graph generation, and graph-conditioned language modelling

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 20 references that are in the map. First posted on arXiv 11 Mar 2026.

Builds on

12 of 20 references.

References · 12

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2017Graph Attention Networkscited by 8,854
  5. 2017Inductive Representation Learning on Large Graphscited by 6,284
  6. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  7. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  8. 2018SimGNN: A Neural Network Approach to Fast Graph Similarity Computationcited by 89
  9. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  10. 2022A Survey on Graph Representation Learning Methodscited by 66
  11. 2024Graph Edit Distance with General Costs Using Neural Set Divergencecited by 10
  12. 2025Representation of the structure of graphs by sequences of instructionscited by 2

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