# node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching

arXiv: https://arxiv.org/abs/1904.08572 · HTML: https://mlsanity.com/paper/1904.08572/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2019.

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

- [Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation](https://mlsanity.com/paper/2110.14509/) (2021)
- [MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learning](https://mlsanity.com/paper/2206.09280/) (2022)
- [A Survey on Role-Oriented Network Embedding](https://mlsanity.com/paper/2107.08379/) (2021)
- [Efficient Network Embedding by Approximate Equitable Partitions](https://mlsanity.com/paper/2409.10160/) (2024)
- [Representation Learning on Heterostructures via Heterogeneous Anonymous Walks](https://mlsanity.com/paper/2201.06972/) (2022)
- [Privacy Aware Experiments without Cookies](https://mlsanity.com/paper/2211.03758/) (2022)
- [From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs](https://mlsanity.com/paper/2307.08433/) (2023)
- [Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs](https://mlsanity.com/paper/2407.07712/) (2024)

## Abstract (arXiv)

Identity stitching, the task of identifying and matching various online references (e.g., sessions over different devices and timespans) to the same user in real-world web services, is crucial for personalization and recommendations. However, traditional user stitching approaches, such as grouping or blocking, require quadratic pairwise comparisons between a massive number of user activities, thus posing both computational and storage challenges. Recent works, which are often application-specific, heuristically seek to reduce the amount of comparisons, but they suffer from low precision and recall. To solve the problem in an application-independent way, we take a heterogeneous network-based approach in which users (nodes) interact with content (e.g., sessions, websites), and may have attributes (e.g., location). We propose node2bits, an efficient framework that represents multi-dimensional features of node contexts with binary hashcodes. node2bits leverages feature-based temporal walks to encapsulate short- and long-term interactions between nodes in heterogeneous web networks, and adopts SimHash to obtain compact, binary representations and avoid the quadratic complexity for similarity search. Extensive experiments on large-scale real networks show that node2bits outperforms traditional techniques and existing works that generate real-valued embeddings by up to 5.16% in F1 score on user stitching, while taking only up to 1.56% as much storage.