atlas

Research map / Learning theory and algorithms / Submodular and game algorithms

Minimizing Polarization and Disagreement in Social Networks via Link Recommendation

NeurIPS 2021 · Learning theory and algorithms › Submodular and game algorithms · approach: Submodular influence maximization · 17 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
use FastGreedy approximation instead of exact SpGreedyedge recommendation for P-D index · large networks (k=50)running time: 319.86 -> 101.95 s on CondMatHelped
use FastGreedy approximation instead of exact SpGreedyedge recommendation for P-D index · Reality (k=50)Delta I(G) ratio: -5.7861 -> -5.7183No clear effect
use FastGreedy on small network where exact is cheapedge recommendation · GrQc (k=50)running time: 7.83 -> 11.13 sHurt
greedy algorithms versus exhaustive optimum on small networksSpGreedy and FastGreedy · Karate, Dolphins, Netscience, DiseasomDelta I(G)No clear effect
greedy algorithms versus Random edge selectionSpGreedy and FastGreedy · small networksDelta I(G)Helped
FastGreedy versus degree and betweenness heuristicsedge recommendation · Yeast, GridWorm, Erdos992, RealityDelta I(G)Helped

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy