# Link Prediction in Networks with Nodes Attributes by Similarity Propagation

arXiv: https://arxiv.org/abs/1502.04380 · HTML: https://mlsanity.com/paper/1502.04380/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2015.

## Built on by (most cited first, 2 of 2)

- [Learning Peer Influence Probabilities with Linear Contextual Bandits](https://mlsanity.com/paper/2510.19119/) (2025)
- [Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Prediction](https://mlsanity.com/paper/2307.08877/) (2023)

## Abstract (arXiv)

The problem of link prediction has attracted considerable recent attention from various domains such as sociology, anthropology, information science, and computer sciences. A link prediction algorithm is proposed based on link similarity score propagation by a random walk in networks with nodes attributes. In the algorithm, each link in the network is assigned a transmission probability according to the similarity of the attributes on the nodes connected by the link. The link similarity score between the nodes are then propagated via the links according to their transmission probability. Our experimental results show that it can obtain higher quality results on the networks with node attributes than other algorithms.