Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Substitutability-Based Graph Node Pricing

Huiju Wang, Yuanyuan Gao, Zhengkui Wang, Xiao Yue

TL;DR

This paper tackles graph node pricing in data markets by introducing substitutability as a structural valuation concept. It operationalizes this idea through a dominator-tree framework built on the Lengauer-Tarjan algorithm, combining path similarity and node positional criticality to quantify substitutability and derive node prices. Four algorithms are proposed: a baseline Basic Graph Data Pricing Algorithm, a Dominator Tree-based Algorithm, a Path Similarity-based Substitutability Algorithm, and a MinHash-LSH-based Approximation Algorithm, with extensive experiments showing improved pricing quality and scalability across real networks. The approach yields more realistic valuations for graph data, informing pricing decisions in data-driven environments and addressing key gaps in prior literature that relied on fixed prices or historical propagation data.

Abstract

In the era o fdat commodification,the pricing o fgraph data presents unique challenges that differ significantly from traditional data markets. This paper addresses the critical issue of node pricing within graph structures, an area that has been largely overlooked in existing literature. We introduce a novel pricing mechanism based on the concept of substitutability, inspired by economic principles, to better reflect the ntrinsic value of nodes in a graph. Unlike previous studies that assumed known prices for nodes or subgraphs, our approach emphasizes the structural significance of nodes by employing a dominator tree, utilizing the Lengauer-Tarjan algorithm to extract dominance relationships. This innovative framework allows us to derive a more realistic pricing strategy that accounts for the unique connectivity and roles of nodes within their respective networks. Our comparative experiments demonstrate that the proposed method significantly outperforms existing pricing strategies, yielding high-quality solutions across various datasets. This research aims to contribute to the existing literature by addressing an important gap and providing insights that may assist in the more effective valuation of graph data, potentially supporting improved decision-making in data-driven environments.

Substitutability-Based Graph Node Pricing

TL;DR

This paper tackles graph node pricing in data markets by introducing substitutability as a structural valuation concept. It operationalizes this idea through a dominator-tree framework built on the Lengauer-Tarjan algorithm, combining path similarity and node positional criticality to quantify substitutability and derive node prices. Four algorithms are proposed: a baseline Basic Graph Data Pricing Algorithm, a Dominator Tree-based Algorithm, a Path Similarity-based Substitutability Algorithm, and a MinHash-LSH-based Approximation Algorithm, with extensive experiments showing improved pricing quality and scalability across real networks. The approach yields more realistic valuations for graph data, informing pricing decisions in data-driven environments and addressing key gaps in prior literature that relied on fixed prices or historical propagation data.

Abstract

In the era o fdat commodification,the pricing o fgraph data presents unique challenges that differ significantly from traditional data markets. This paper addresses the critical issue of node pricing within graph structures, an area that has been largely overlooked in existing literature. We introduce a novel pricing mechanism based on the concept of substitutability, inspired by economic principles, to better reflect the ntrinsic value of nodes in a graph. Unlike previous studies that assumed known prices for nodes or subgraphs, our approach emphasizes the structural significance of nodes by employing a dominator tree, utilizing the Lengauer-Tarjan algorithm to extract dominance relationships. This innovative framework allows us to derive a more realistic pricing strategy that accounts for the unique connectivity and roles of nodes within their respective networks. Our comparative experiments demonstrate that the proposed method significantly outperforms existing pricing strategies, yielding high-quality solutions across various datasets. This research aims to contribute to the existing literature by addressing an important gap and providing insights that may assist in the more effective valuation of graph data, potentially supporting improved decision-making in data-driven environments.

Paper Structure

This paper contains 13 sections, 6 equations, 7 figures, 4 tables.

Table of Contents

  1. INTRODUCTION
  2. PROBLEM DEFINITION
  3. GRAPH NODE PRICING ALGORITHM AND OPTIMIZATION
  4. Basic Graph Data Pricing Algorithm
  5. Dominator Tree-based Algorithm
  6. Path Similarity-based Node Substitutability Algorithm.
  7. Approximation Algorithm to Node Substitutability
  8. EXPERIMENTS
  9. Datasets
  10. Experimental Settings
  11. Experimental Results
  12. RELATED WORK
  13. CONCLUSION

Figures (7)

  • Figure 1: Example of Node Path Similarity.
  • Figure 2: Example of Node Substitutability.
  • Figure 3: The effect of transforming a directed graph into a dominator tree for graph data.
  • Figure 4: Exp.A: Effects of pricing based on substitutability.
  • Figure 5: Exp.B: Effects of dominator tree
  • ...and 2 more figures