# motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks

arXiv: https://arxiv.org/abs/1908.08227 · HTML: https://mlsanity.com/paper/1908.08227/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2019.

## Built on by (most cited first, 6 of 6)

- [Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs](https://mlsanity.com/paper/2210.13710/) (2022)
- [Motif Graph Neural Network](https://mlsanity.com/paper/2112.14900/) (2021)
- [Financial Default Prediction via Motif-preserving Graph Neural Network with Curriculum Learning](https://mlsanity.com/paper/2403.06482/) (2024)
- [Studying and Improving Graph Neural Network-based Motif Estimation](https://mlsanity.com/paper/2506.15709/) (2025)
- [TME-BNA: Temporal Motif-Preserving Network Embedding with Bicomponent Neighbor Aggregation](https://mlsanity.com/paper/2110.13596/) (2021)
- [Capturing Fine-grained Semantics in Contrastive Graph Representation Learning](https://mlsanity.com/paper/2304.11658/) (2023)

## Abstract (arXiv)

Recent years have witnessed a surge of interest in machine learning on graphs and networks with applications ranging from vehicular network design to IoT traffic management to social network recommendations. Supervised machine learning tasks in networks such as node classification and link prediction require us to perform feature engineering that is known and agreed to be the key to success in applied machine learning. Research efforts dedicated to representation learning, especially representation learning using deep learning, has shown us ways to automatically learn relevant features from vast amounts of potentially noisy, raw data. However, most of the methods are not adequate to handle heterogeneous information networks which pretty much represents most real-world data today. The methods cannot preserve the structure and semantic of multiple types of nodes and links well enough, capture higher-order heterogeneous connectivity patterns, and ensure coverage of nodes for which representations are generated. We propose a novel efficient algorithm, motif2vec that learns node representations or embeddings for heterogeneous networks. Specifically, we leverage higher-order, recurring, and statistically significant network connectivity patterns in the form of motifs to transform the original graph to motif graph(s), conduct biased random walk to efficiently explore higher order neighborhoods, and then employ heterogeneous skip-gram model to generate the embeddings. Unlike previous efforts that uses different graph meta-structures to guide the random walk, we use graph motifs to transform the original network and preserve the heterogeneity. We evaluate the proposed algorithm on multiple real-world networks from diverse domains and against existing state-of-the-art methods on multi-class node classification and link prediction tasks, and demonstrate its consistent superiority over prior work.