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ESND: An Embedding-based Framework for Signed Network Dismantling

Chenwei Xie, Chuang Liu, Cong Li, Xiu-Xiu Zhan, Xiang Li

TL;DR

This work proposes an embedding-based algorithm, namely ESND, to solve the signed network dismantling problem, and examines the impact of sign proportions on network robustness via ESND, observing that networks with a high ratio of negative edges are generally easier to dismantle than networks with high positive edges.

Abstract

Network dismantling aims to maximize the disintegration of a network by removing a specific set of nodes or edges and is applied to various tasks in diverse domains, such as cracking down on crime organizations, delaying the propagation of rumors, and blocking the transmission of viruses. Most of the current network dismantling methods are tailored for unsigned networks, which only consider the connection between nodes without evaluating the nature of the relationships, such as friendship/hostility, enhancing/repressing, and trust/distrust. We here propose an embedding-based algorithm, namely ESND, to solve the signed network dismantling problem. The algorithm generally iterates the following four steps, i.e., giant component detection, network embedding, node clustering, and removal node selection. To illustrate the efficacy and stability of ESND, we conduct extensive experiments on six signed network datasets as well as null models, and compare the performance of our method with baselines. Experimental results consistently show that the proposed ESND is superior to the baselines and displays stable performance with the change in the network structure. Additionally, we examine the impact of sign proportions on network robustness via ESND, observing that networks with a high ratio of negative edges are generally easier to dismantle than networks with high positive edges.

ESND: An Embedding-based Framework for Signed Network Dismantling

TL;DR

This work proposes an embedding-based algorithm, namely ESND, to solve the signed network dismantling problem, and examines the impact of sign proportions on network robustness via ESND, observing that networks with a high ratio of negative edges are generally easier to dismantle than networks with high positive edges.

Abstract

Network dismantling aims to maximize the disintegration of a network by removing a specific set of nodes or edges and is applied to various tasks in diverse domains, such as cracking down on crime organizations, delaying the propagation of rumors, and blocking the transmission of viruses. Most of the current network dismantling methods are tailored for unsigned networks, which only consider the connection between nodes without evaluating the nature of the relationships, such as friendship/hostility, enhancing/repressing, and trust/distrust. We here propose an embedding-based algorithm, namely ESND, to solve the signed network dismantling problem. The algorithm generally iterates the following four steps, i.e., giant component detection, network embedding, node clustering, and removal node selection. To illustrate the efficacy and stability of ESND, we conduct extensive experiments on six signed network datasets as well as null models, and compare the performance of our method with baselines. Experimental results consistently show that the proposed ESND is superior to the baselines and displays stable performance with the change in the network structure. Additionally, we examine the impact of sign proportions on network robustness via ESND, observing that networks with a high ratio of negative edges are generally easier to dismantle than networks with high positive edges.
Paper Structure (16 sections, 5 equations, 7 figures, 2 tables)

This paper contains 16 sections, 5 equations, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Giant Connected Component Detection
  4. Signed Network Embedding (SiNE)
  5. Node clustering
  6. Node Elimination
  7. Baselines
  8. Experiments
  9. Datasets
  10. Performance Evaluation Metric
  11. Parameter Analysis
  12. Experimental Results
  13. Results on Real Signed Networks
  14. Results on Null Models of the Signed Network
  15. Impact of the signs on network robustness
  16. ...and 1 more sections

Figures (7)

  • Figure 1: Framework of ESND. The solid black lines represent positive edges, the red dashed lines indicate negative edges, $q$ represents the fraction of the removed nodes, and $q_r$ is a threshold value indicating when we will stop the algorithm.
  • Figure 2: Performance of ESND under different parameter settings. The x-axis indicates the number of clusters $k$ and the y-axis shows the performance of network dismantling. Different curves show the use of different values of dimension $d$ in the embedding procedure. We show results for: (a)Bitcoinalpha; (b)Bitcoinotc; (c)WikiVote; (d)Slashdot; (e)Reddit; (f)Epinions.
  • Figure 3: Comparison of ESND with baselines on signed networks: (a)Bitcoinalpha; (b)Bitcoinotc; (c)WikiVote; (d)Slashdot; (e)Reddit; (f)Epinions. X-axis shows the fraction of nodes removed and y-axis means the ratio of nodes in the giant component after node removal.
  • Figure 4: Analysis of differences between removal node sequences generated by different methods. Each square represents the Kendall correlation coefficient between the removed node sequences generated by the corresponding pair of methods. We show the results for the following signed networks: (a) Bitcoinalpha; (b) Bitcoinotc; (c) WikiVote; (d) Slashdot; (e) Reddit; (f) Epinions.
  • Figure 5: Toy examples of null models of a signed network. Solid lines represent positive edges, and dotted lines represent negative edges.
  • ...and 2 more figures