# Harnessing the Power of Ego Network Layers for Link Prediction in Online Social Networks

arXiv: https://arxiv.org/abs/2109.09190 · HTML: https://mlsanity.com/paper/2109.09190/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 67 references that are in the map.
- First posted on arXiv 19 Sep 2021.

## Builds on (most cited first, 15 of 15)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [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)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [Network Representation Learning: A Survey](https://mlsanity.com/paper/1801.05852/) (2017)
- [Line Graph Neural Networks for Link Prediction](https://mlsanity.com/paper/2010.10046/) (2020)
- [Evaluating Link Prediction Methods](https://mlsanity.com/paper/1505.04094/) (2015)
- [E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction](https://mlsanity.com/paper/1902.08329/) (2019)
- [An Evolutionary Algorithm Approach to Link Prediction in Dynamic Social Networks](https://mlsanity.com/paper/1304.6257/) (2013)
- [Twitter and the Press: an Ego-Centred Analysis](https://mlsanity.com/paper/1803.08858/) (2018)
- [Time as a limited resource: Communication Strategy in Mobile Phone Networks](https://mlsanity.com/paper/1301.2464/) (2013)
- [Evolution of Ego-networks in Social Media with Link Recommendations](https://mlsanity.com/paper/1702.01398/) (2017)
- [The academic wanderer: structure of collaboration network and relation with research performance](https://mlsanity.com/paper/2105.06840/) (2021)

## Built on by (most cited first, 3 of 3)

- [MIDDAG: Where Does Our News Go? Investigating Information Diffusion via Community-Level Information Pathways](https://mlsanity.com/paper/2310.02529/) (2023)
- [Structural invariants and semantic fingerprints in the "ego network" of words](https://mlsanity.com/paper/2203.00588/) (2022)
- [Cooperative Causal GraphSAGE](https://mlsanity.com/paper/2505.14748/) (2025)

## Abstract (arXiv)

Being able to recommend links between users in online social networks is important for users to connect with like-minded individuals as well as for the platforms themselves and third parties leveraging social media information to grow their business. Predictions are typically based on unsupervised or supervised learning, often leveraging simple yet effective graph topological information, such as the number of common neighbors. However, we argue that richer information about personal social structure of individuals might lead to better predictions. In this paper, we propose to leverage well-established social cognitive theories to improve link prediction performance. According to these theories, individuals arrange their social relationships along, on average, five concentric circles of decreasing intimacy. We postulate that relationships in different circles have different importance in predicting new links. In order to validate this claim, we focus on popular feature-extraction prediction algorithms (both unsupervised and supervised) and we extend them to include social-circles awareness. We validate the prediction performance of these circle-aware algorithms against several benchmarks (including their baseline versions as well as node-embedding- and GNN-based link prediction), leveraging two Twitter datasets comprising a community of video gamers and generic users. We show that social-awareness generally provides significant improvements in the prediction performance, beating also state-of-the-art solutions like node2vec and SEAL, and without increasing the computational complexity. Finally, we show that social-awareness can be used in place of using a classifier (which may be costly or impractical) for targeting a specific category of users.