Encoding Node Diffusion Competence and Role Significance for Network Dismantling
Jiazheng Zhang, Bang Wang

TL;DR
This paper introduces an unsupervised network dismantling method that combines node diffusion competence and role significance to identify critical nodes more effectively, outperforming existing approaches.
Contribution
It proposes a novel framework, DCRS, that encodes and fuses functional and topological node importance for improved network dismantling.
Findings
Outperforms state-of-the-art methods in network dismantling tasks.
Requires fewer nodes to effectively dismantle networks.
Works well on both real-world and synthetic networks.
Abstract
Percolation theory shows that removing a small fraction of critical nodes can lead to the disintegration of a large network into many disconnected tiny subnetworks. The network dismantling task focuses on how to efficiently select the least such critical nodes. Most existing approaches focus on measuring nodes' importance from either functional or topological viewpoint. Different from theirs, we argue that nodes' importance can be measured from both of the two complementary aspects: The functional importance can be based on the nodes' competence in relaying network information; While the topological importance can be measured from nodes' regional structural patterns. In this paper, we propose an unsupervised learning framework for network dismantling, called DCRS, which encodes and fuses both node diffusion competence and role significance. Specifically, we propose a graph diffusion…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComplex Network Analysis Techniques · Advanced Graph Neural Networks · Opinion Dynamics and Social Influence
