Community-Focused Resilience: A Multi-Domain Infrastructure Network Dataset

Citation Author(s):
Adaeze
Okeukwu-Ogbonnaya
Submitted by:
Adaeze Okeukwu
Last updated:
Mon, 02/03/2025 - 16:33
DOI:
10.21227/2m92-9f70
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Abstract 

This dataset provides a GraphML‐based representation of an interdependent infrastructure system, covering power, water, transportation, and community infrastructures. Each node is annotated with key attributes such as resource type, criticality, societal vulnerability score (SVS), and capacity—while directed edges capture the flow or dependency relationships among these resources. The dataset facilitates resilience and recovery analysis by enabling repair order simulation, based on certain metrics‐based prioritization. Researchers may use the dataset to visualize, manipulate, or extend the network structure and attributes. The inclusion of community-facing nodes (e.g., hospitals, schools) allows for equity considerations during disruptions, making the dataset particularly valuable for research in disaster risk management and interdependent system modeling. By providing a thorough mapping of nodes, edges, and community dimensions, this dataset supports a wide range of infrastructure resilience research questions, from comparing different repair heuristics to analyzing the impact of criticality versus social vulnerability in restoration strategies.

Instructions: 

1. Load the GraphML file:

   - In Python with NetworkX:

     import networkx as nx

     G = nx.read_graphml("community_hfg_model.graphml")

 

   - Or in a graph editor (e.g. Gephi) for visual analysis.

 

2. Explore node attributes:

   for node, attrs in G.nodes(data=True):

       print(node, attrs)

 

3. Perform analyses:

   - Typical usage involves resilience simulations.

   - You can run BFS from “Generator1” or nodes tagged “WaterTreatmentPlant” to evaluate dependencies or interdependencies.

 

4. Incorporate the dataset into your code:

   - Place `community_hfg_model.graphml` in the same directory or specify the path:

     python main.py --graph "community_hfg_model.graphml" --infra "power" ...

 

5. Modify or extend:

   - You can change node attributes, update edges, or add new layers to explore alternative scenarios.

   - Tools like Gephi let you edit the graph visually.

 

Funding Agency: 
National Science Foundation
Grant Number: 
2148878

Comments

Data is uploaded.

Submitted by Adaeze Okeukwu on Mon, 02/03/2025 - 16:30

Documentation

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