# MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network

## TL;DR

MCNE is an innovative end-to-end framework that learns multiple conditional network representations to better capture diverse user preferences in social networks, improving upon existing single-vector methods.

## Contribution

The paper introduces MCNE, a novel framework that models multiple user preferences simultaneously using conditional embeddings, attention mechanisms, and multi-task learning in social network representation.

## Key findings

- Significantly outperforms state-of-the-art baselines.
- Effectively captures multi-aspect user preferences.
- Supports visualization and transfer learning with high interpretability.

## Abstract

Recently, the Network Representation Learning (NRL) techniques, which represent graph structure via low-dimension vectors to support social-oriented application, have attracted wide attention. Though large efforts have been made, they may fail to describe the multiple aspects of similarity between social users, as only a single vector for one unique aspect has been represented for each node. To that end, in this paper, we propose a novel end-to-end framework named MCNE to learn multiple conditional network representations, so that various preferences for multiple behaviors could be fully captured. Specifically, we first design a binary mask layer to divide the single vector as conditional embeddings for multiple behaviors. Then, we introduce the attention network to model interaction relationship among multiple preferences, and further utilize the adapted message sending and receiving operation of graph neural network, so that multi-aspect preference information from high-order neighbors will be captured. Finally, we utilize Bayesian Personalized Ranking loss function to learn the preference similarity on each behavior, and jointly learn multiple conditional node embeddings via multi-task learning framework. Extensive experiments on public datasets validate that our MCNE framework could significantly outperform several state-of-the-art baselines, and further support the visualization and transfer learning tasks with excellent interpretability and robustness.