# An Attention-based Collaboration Framework for Multi-View Network Representation Learning

arXiv: https://arxiv.org/abs/1709.06636 · HTML: https://mlsanity.com/paper/1709.06636/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2017.

## Built on by (most cited first, 30 of 31)

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- [Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating](https://mlsanity.com/paper/2211.14065/) (2022)
- [Incomplete Multi-view Clustering via Prototype-based Imputation](https://mlsanity.com/paper/2301.11045/) (2023)
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- [Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding](https://mlsanity.com/paper/2110.07582/) (2021)
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- [Heterogeneous Graph Learning for Multi-modal Medical Data Analysis](https://mlsanity.com/paper/2211.15158/) (2022)
- [Upper Bounding Barlow Twins: A Novel Filter for Multi-Relational Clustering](https://mlsanity.com/paper/2312.14066/) (2023)
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- [Balanced Multi-Relational Graph Clustering](https://mlsanity.com/paper/2407.16863/) (2024)
- [Recommender systems based on graph embedding techniques: A comprehensive review](https://mlsanity.com/paper/2109.09587/) (2021)
- [High-order Multi-view Clustering for Generic Data](https://mlsanity.com/paper/2209.10838/) (2022)
- [Estimating Treatment Effects Under Heterogeneous Interference](https://mlsanity.com/paper/2309.13884/) (2023)
- [Instant Representation Learning for Recommendation over Large Dynamic Graphs](https://mlsanity.com/paper/2305.18622/) (2023)
- [Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT)](https://mlsanity.com/paper/2202.09021/) (2022)
- [Deep Partial Multiplex Network Embedding](https://mlsanity.com/paper/2203.02656/) (2022)
- [Learning with Multigraph Convolutional Filters](https://mlsanity.com/paper/2210.16272/) (2022)
- [Graph Mining for Cybersecurity: A Survey](https://mlsanity.com/paper/2304.00485/) (2023)
- [Urban Region Profiling via A Multi-Graph Representation Learning Framework](https://mlsanity.com/paper/2202.02074/) (2022)
- [Learning Multiplex Representations on Text-Attributed Graphs with One Language Model Encoder](https://mlsanity.com/paper/2310.06684/) (2023)
- [Multi-Relational Graph Neural Network for Out-of-Domain Link Prediction](https://mlsanity.com/paper/2403.11292/) (2024)
- [Attention-based graph neural networks: a survey](https://mlsanity.com/paper/2605.08679/) (2026)
- [Deep Contrastive Multiview Network Embedding](https://mlsanity.com/paper/2108.08296/) (2021)
- [MultiSAGE: a multiplex embedding algorithm for inter-layer link prediction](https://mlsanity.com/paper/2206.13223/) (2022)
- [Audience Expansion for Multi-show Release Based on an Edge-prompted Heterogeneous Graph Network](https://mlsanity.com/paper/2304.05474/) (2023)
- [Simple Multigraph Convolution Networks](https://mlsanity.com/paper/2403.05014/) (2024)
- [MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embedding](https://mlsanity.com/paper/2403.19246/) (2024)
- [Collaborative Team Recognition: A Core Plus Extension Structure](https://mlsanity.com/paper/2406.06617/) (2024)

## Abstract (arXiv)

Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations. During the voting process, an attention mechanism is introduced, which enables each node to focus on the most informative views. Experimental results on real-world networks show that the proposed approach outperforms existing state-of-the-art approaches for network representation learning with a single view and other competitive approaches with multiple views.