Modeling User Propagation Ability for Social Recommendation and Influence Maximization in Games

This article presents a comprehensive study on leveraging user propagation ability metrics for friend recommendation and influence maximization in gaming environments, introducing a conversion‑funnel‑aware diffusion model, novel influence‑maximization variants, efficient greedy algorithms, and extensive offline and online experiments that demonstrate significant performance gains over traditional methods.

Gaminggraph algorithmsinfluence maximization

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Modeling User Propagation Ability for Social Recommendation and Influence Maximization in Games

Tencent Cloud Developer

Jun 28, 2024 · Big Data

Capacity-Constrained Influence Maximization: Algorithms and Applications

The paper introduces Capacity‑Constrained Influence Maximization (CIM), a framework that selects up to k neighbors per active user to maximize spread under node capacity limits, proposes MG‑Greedy and RR‑Greedy algorithms with ≥½ approximation, and demonstrates the near‑linear RR‑OPIM+ method’s superior accuracy and speed on large social networks and a Tencent game recommendation system.

Big DataCapacity ConstraintKDD 2023

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Capacity-Constrained Influence Maximization: Algorithms and Applications

Model Perspective

Oct 8, 2023 · Fundamentals

How Do Information Cascades Spread in Social Networks? Models & Simulations

This article examines information diffusion in social networks by introducing the Independent Cascade and Linear Threshold models, discussing network structures, the influence maximization problem, and presenting a simulation experiment that quantifies spread under varying probabilities.

independent cascadeinfluence maximizationinformation diffusion

0 likes · 9 min read