# Producing a Unified Graph Representation from Multiple Social Network Views

arXiv: https://arxiv.org/abs/1301.5809 · HTML: https://mlsanity.com/paper/1301.5809/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2013.

## Built on by (most cited first, 8 of 8)

- [Popularity Adjusted Block Models are Generalized Random Dot Product Graphs](https://mlsanity.com/paper/2109.04010/) (2021)
- [Sparse Subspace Clustering in Diverse Multiplex Network Model](https://mlsanity.com/paper/2206.07602/) (2022)
- [Privacy-Preserving Community Detection for Locally Distributed Multiple Networks](https://mlsanity.com/paper/2306.15709/) (2023)
- [Learning Sparse Graphs with a Core-periphery Structure](https://mlsanity.com/paper/2110.04022/) (2021)
- [High-order Order Proximity-Incorporated, Symmetry and Graph-Regularized Nonnegative Matrix Factorization for Community Detection](https://mlsanity.com/paper/2203.03876/) (2022)
- [Generative Models and Learning Algorithms for Core-Periphery Structured Graphs](https://mlsanity.com/paper/2210.01489/) (2022)
- [A Deep Latent Space Model for Graph Representation Learning](https://mlsanity.com/paper/2106.11721/) (2021)
- [T-GINEE: A Tensor-Based Multilayer Graph Representation Learning](https://mlsanity.com/paper/2605.28300/) (2026)

## Abstract (arXiv)

In many social networks, several different link relations will exist between the same set of users. Additionally, attribute or textual information will be associated with those users, such as demographic details or user-generated content. For many data analysis tasks, such as community finding and data visualisation, the provision of multiple heterogeneous types of user data makes the analysis process more complex. We propose an unsupervised method for integrating multiple data views to produce a single unified graph representation, based on the combination of the k-nearest neighbour sets for users derived from each view. These views can be either relation-based or feature-based. The proposed method is evaluated on a number of annotated multi-view Twitter datasets, where it is shown to support the discovery of the underlying community structure in the data.