# OFFER: A Motif Dimensional Framework for Network Representation Learning

arXiv: https://arxiv.org/abs/2008.12010 · HTML: https://mlsanity.com/paper/2008.12010/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [Graph Learning: A Survey](https://mlsanity.com/paper/2105.00696/) (2021)
- [Higher-order Structure Based Anomaly Detection on Attributed Networks](https://mlsanity.com/paper/2406.04690/) (2024)
- [Graph Augmentation Learning](https://mlsanity.com/paper/2203.09020/) (2022)
- [Subgraph Networks Based Contrastive Learning](https://mlsanity.com/paper/2306.03506/) (2023)
- [PANDORA: Deep graph learning based COVID-19 infection risk level forecasting](https://mlsanity.com/paper/2406.06618/) (2024)

## Abstract (arXiv)

Aiming at better representing multivariate relationships, this paper investigates a motif dimensional framework for higher-order graph learning. The graph learning effectiveness can be improved through OFFER. The proposed framework mainly aims at accelerating and improving higher-order graph learning results. We apply the acceleration procedure from the dimensional of network motifs. Specifically, the refined degree for nodes and edges are conducted in two stages: (1) employ motif degree of nodes to refine the adjacency matrix of the network; and (2) employ motif degree of edges to refine the transition probability matrix in the learning process. In order to assess the efficiency of the proposed framework, four popular network representation algorithms are modified and examined. By evaluating the performance of OFFER, both link prediction results and clustering results demonstrate that the graph representation learning algorithms enhanced with OFFER consistently outperform the original algorithms with higher efficiency.