# Diversity Preference-Aware Link Recommendation for Online Social Networks

arXiv: https://arxiv.org/abs/2205.10689 · HTML: https://mlsanity.com/paper/2205.10689/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 73 references that are in the map.
- First posted on arXiv 21 May 2022.

## Builds on (most cited first, 18 of 18)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://mlsanity.com/paper/1902.10191/) (2019)
- [Recent Advances in Diversified Recommendation](https://mlsanity.com/paper/1905.06589/) (2019)
- [Predicting Adoption Probabilities in Social Networks](https://mlsanity.com/paper/1309.6369/) (2013)
- [Learning Semantic Similarity for Very Short Texts](https://mlsanity.com/paper/1512.00765/) (2015)
- [Utility-based Link Recommendation for Online Social Networks](https://mlsanity.com/paper/1512.06840/) (2015)
- [A Survey of Link Recommendation for Social Networks: Methods, Theoretical Foundations, and Future Research Directions](https://mlsanity.com/paper/1511.01868/) (2015)
- [Exploring Hierarchies in Online Social Networks](https://mlsanity.com/paper/1502.04220/) (2015)

## Built on by (most cited first, 1 of 1)

- [Preference Construction: A Bayesian Interactive Preference Elicitation Framework Based on Monte Carlo Tree Search](https://mlsanity.com/paper/2503.15150/) (2025)

## Abstract (arXiv)

Link recommendation, which recommends links to connect unlinked online social network users, is a fundamental social network analytics problem with ample business implications. Existing link recommendation methods tend to recommend similar friends to a user but overlook the user's diversity preference, although social psychology theories suggest the criticality of diversity preference to link recommendation performance. In recommender systems, a field related to link recommendation, a number of diversification methods have been proposed to improve the diversity of recommended items. Nevertheless, diversity preference is distinct from diversity studied by diversification methods. To address these research gaps, we define and operationalize the concept of diversity preference for link recommendation and propose a new link recommendation problem: the diversity preference-aware link recommendation problem. We then analyze key properties of the new link recommendation problem and develop a novel link recommendation method to solve the problem. Using two large-scale online social network data sets, we conduct extensive empirical evaluations to demonstrate the superior performance of our method over representative diversification methods adapted for link recommendation as well as state-of-the-art link recommendation methods.