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arxiv: 2412.09404 · v3 · submitted 2024-12-12 · 💻 cs.SI · cs.LG

Opinion de-polarization in social networks with GNNs

Konstantinos Mylonas , Thrasyvoulos Spyropoulos This is my paper

Pith reviewed 2026-05-23 07:12 UTC · model grok-4.3

classification 💻 cs.SI cs.LG
keywords polarizationecho chambersgraph neural networkssocial networksopinion dynamicsde-polarizationuser selectionmoderation
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The pith

In networks split into two echo chambers, moderating the opinions of a small set of users identified by a graph neural network minimizes overall polarization.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that when users in a two-chamber social network adopt moderate opinions on a topic, the network's polarization drops. It then builds an algorithm that uses a graph neural network to select which K users to moderate in order to achieve the largest possible reduction. The method works from network connections alone and scales to large graphs where other selection techniques become slow. A reader would care because it identifies concrete leverage points for easing divides without requiring changes from every user. If the claim holds, targeted moderation of the right users offers a practical route to lower polarization.

Core claim

The central claim is that if some users adopt a moderate opinion about a topic, the polarization of the network decreases, and a graph neural network can be used to identify a good set of K users such that moderating them minimizes polarization in networks with two echo chambers.

What carries the argument

A graph neural network that selects the K users whose moderation produces the greatest drop in polarization.

If this is right

  • Polarization falls when the chosen users shift to moderate positions.
  • The graph neural network approach runs faster on large graphs than methods that do not use it.
  • Selection depends only on the pattern of connections, without needing extra opinion or content data.
  • The same users serve as effective targets across different topics in the same network structure.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The technique could be tested by measuring real polarization changes after platform prompts that encourage moderation in the identified users.
  • Extending the model to networks with more than two chambers would require checking whether the same selection logic still finds effective moderators.
  • Combining the network-only GNN with limited content signals might improve selection accuracy without losing scalability.

Load-bearing premise

That forcing a small number of users to adopt moderate opinions will reliably produce a drop in the network's measured polarization.

What would settle it

Apply the selected K users' moderation in a simulation or real network and check whether the polarization value falls below the value obtained from random or baseline selections of the same size.

Figures

Figures reproduced from arXiv: 2412.09404 by Konstantinos Mylonas, Thrasyvoulos Spyropoulos.

Figure 1
Figure 1. Figure 1: Graphical illustration of the pipeline that finds the best node at a timestep The online part of our method is what we actually use to solve the ModerateExpressed problem. It builds upon and aims to improve, in terms of time, the previously discussed GreedyExt algorithm. The core principle behind both al￾gorithms is iterative: at each timestep, we aim to select the best node to add to the solution set base… view at source ↗
Figure 2
Figure 2. Figure 2: Example of graph generated with the DCSBM model.Node size reflects degree, with larger nodes having higher degrees. Blue and red nodes represent two echo chambers. We construct 128 graphs with the DCSBM model, where each graph consists of 1000 nodes. The degrees are drawn from a power law distribution, which is common in social networks according to (Newman, 2010). Next we assign internal opinions to nodes… view at source ↗
Figure 3
Figure 3. Figure 3: LiveJournal Graph si = +1 if she supports a party that is left on the political spectrum and si = −1 otherwise. Because the retweet graph consists of 44k nodes, we work with a smaller sample of 5k nodes which is depicted in [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Retweet graph for the wiretaping scandal with 5k nodes 8 [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Polarization of each algorithm per dataset. The orange bars show the initial polarization of each network. The blue and green bars show the final polarization that GreedyExt and GNN￾GreedyExt achieve Inference speed So far, we have only discussed how the two algorithms com￾pare, in terms of the final polarization that they achieve. As we have already mentioned, the GreedyExt algorithm, is not efficient for… view at source ↗
Figure 7
Figure 7. Figure 7: Required time per algorithm for the LiveJournal and Wiretaping datasets As the picture shows, the GNN-GreedyExt outperforms the GreedyExt in terms of time. Similar results hold for the 9 [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Required time per algorithm for graphs with various sizes Moreover, the time advantage of the GNN-GreedyExt against the GreedyExt, is larger, for larger graphs. In other words, it increases with respect to the size of the underlying graph. In the examples that we have shown where large graphs were involved, the LiveJournal and the Wiretaping datasets, the GNN-GreedyExt is ×10 and ×16 times faster respectiv… view at source ↗
read the original abstract

Nowadays, social media is the ground for political debate and exchange of opinions. There is a significant amount of research that suggests that social media are highly polarized. A phenomenon that is commonly observed is the echo chamber structure, where users are organized in polarized communities and form connections only with similar-minded individuals, limiting themselves to consume specific content. In this paper we explore a way to decrease the polarization of networks with two echo chambers. Particularly, we observe that if some users adopt a moderate opinion about a topic, the polarization of the network decreases. Based on this observation, we propose an efficient algorithm to identify a good set of K users, such that if they adopt a moderate stance around a topic, the polarization is minimized. Our algorithm employs a Graph Neural Network and thus it can handle large graphs more effectively than other approaches

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript claims that in social networks with two echo chambers, moderating the opinions of a small set of K users decreases overall polarization, and proposes a GNN-based algorithm to select such users efficiently for large graphs, outperforming other approaches by relying on network structure alone.

Significance. If the observation that moderating K users reliably reduces polarization holds and the GNN heuristic is effective without additional opinion data, the work could offer a scalable method for de-polarization in large networks. The use of GNNs for this selection task is a potentially practical strength if empirically validated.

major comments (2)
  1. [Abstract] Abstract: The central claim that the proposed GNN algorithm identifies a set of K users whose moderation minimizes polarization lacks any supporting details on the polarization metric, GNN architecture, training, or selection procedure, preventing evaluation of whether the method actually achieves the stated goal or outperforms alternatives.
  2. [Abstract] Abstract: No experiments, baselines, datasets, or quantitative results are described to support the claim that moderating K users decreases polarization or that the GNN handles large graphs more effectively, which is load-bearing for the empirical contribution.
minor comments (1)
  1. [Abstract] Abstract: The phrasing 'the polarization of the network decreases' and 'the polarization is minimized' should be clarified with a precise definition of the polarization measure used.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their comments. We address each major comment below. All comments concern the abstract, which we agree can be strengthened by incorporating additional summary information from the body of the paper.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that the proposed GNN algorithm identifies a set of K users whose moderation minimizes polarization lacks any supporting details on the polarization metric, GNN architecture, training, or selection procedure, preventing evaluation of whether the method actually achieves the stated goal or outperforms alternatives.

    Authors: The polarization metric, GNN architecture, training details, and selection procedure are defined and explained in the main sections of the manuscript. We agree, however, that the abstract would be improved by briefly summarizing these elements so that the central claim can be evaluated without first reading the full text. We will revise the abstract to include such a summary. revision: yes

  2. Referee: [Abstract] Abstract: No experiments, baselines, datasets, or quantitative results are described to support the claim that moderating K users decreases polarization or that the GNN handles large graphs more effectively, which is load-bearing for the empirical contribution.

    Authors: The manuscript contains an experimental evaluation with baselines, datasets, and quantitative results demonstrating both the polarization reduction and scalability. We acknowledge that these are not referenced in the current abstract. We will revise the abstract to include a concise statement of the experimental findings and their implications. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical heuristic without derivation chain

full rationale

The paper reports an empirical observation that moderating a small set of users reduces a polarization metric in two-chamber networks, then proposes a GNN heuristic to select such users for large graphs. No equations, first-principles derivations, fitted parameters renamed as predictions, or self-citation chains appear in the abstract or high-level description. The central claim rests on simulation/data validation of the moderation effect plus algorithmic performance, not on any algebraic reduction to its own inputs. This is the common case of a self-contained empirical contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only; no free parameters, axioms, or invented entities are described.

pith-pipeline@v0.9.0 · 5668 in / 912 out tokens · 34892 ms · 2026-05-23T07:12:50.626667+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. PACIFIER: Pacing Opinion Depolarization via a Unified Graph Learning Framework

    cs.SI 2026-02 unverdicted novelty 7.0

    PACIFIER is a graph RL framework that matches analytical solvers for minimizing polarization and outperforms baselines across multiple intervention regimes on real Twitter networks up to 155k nodes by training on smal...

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