Link Recommendation Algorithm for Social Influence Maximization

核心概念

The authors propose an algorithm, AIS, for link recommendation to enhance social influence diffusion, providing a high-probability approximate solution with theoretical guarantees.

摘要

The paper addresses the challenge of link recommendation for social influence maximization in online social networks. It introduces the IMA problem and presents the AIS algorithm as a solution. The algorithm aims to select edges to augment the influence spread of a seed set efficiently. By leveraging reverse influence sampling and efficient estimators, AIS achieves theoretical guarantees and outperforms existing methods in experiments on various datasets.

統計資料

The IMA problem is proven to be NP-hard.
AIS provides a (1 − 1/e − 𝜀)-approximate solution with a high probability of 1 − 𝛿.
AIS runs in 𝑂(𝑘2(𝑚 +𝑛) log(𝑛/𝛿)/𝜀2 +𝑘 |𝐸C|) time.

引述

"The combination of link recommendation with information diffusion in OSNs opens up new opportunities for product marketing."
"In contrast to previous works on influence maximization, the authors focus on recommending links that can augment social influence."

從以下內容提煉的關鍵洞見

Link Recommendation to Augment Influence Diffusion with Provable Guarantees

by Xiaolong Che... 於 arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.19189.pdf

深入探究

How can the AIS algorithm be adapted for different types of online social networks

The AIS algorithm can be adapted for different types of online social networks by customizing the candidate edge set 𝐸C and seed set 𝑆 based on the specific characteristics of each network. For example, in a network where user interactions are primarily driven by content similarity, the propagation probability 𝑝𝑢,𝑣 could be determined based on content relevance between users. In a network where connections are influenced by geographic proximity, the propagation probability could be calculated using location data. By tailoring these parameters to match the dynamics of different social networks, the AIS algorithm can effectively optimize link recommendations for influence maximization.

What are the potential ethical implications of using algorithms like AIS for targeted marketing

Using algorithms like AIS for targeted marketing raises several ethical implications. One concern is privacy infringement, as these algorithms often rely on extensive user data to make personalized recommendations. There is also a risk of creating filter bubbles or echo chambers, where users are only exposed to information that aligns with their existing beliefs or preferences. This can lead to polarization and limit exposure to diverse perspectives. Additionally, there may be issues related to transparency and accountability in how these algorithms operate and make decisions about which links to recommend.

How might incorporating user preferences or biases impact the effectiveness of link recommendations in social networks

Incorporating user preferences or biases into link recommendation algorithms can have both positive and negative impacts on effectiveness. On one hand, considering user preferences can enhance personalization and increase engagement with recommended links. By leveraging insights into individual interests and behaviors, algorithms can deliver more relevant suggestions that resonate with users. However, if not carefully managed, biases in user data or feedback mechanisms could lead to skewed recommendations that reinforce stereotypes or discriminatory practices. It's crucial for developers to implement safeguards against bias amplification and regularly evaluate algorithm performance from an ethical standpoint.