Accurately Modeling Biased Random Walks on Weighted Graphs Using Node2vec+

09/15/2021
by   Renming Liu, et al.
9

Node embedding is a powerful approach for representing the structural role of each node in a graph. Node2vec is a widely used method for node embedding that works by exploring the local neighborhoods via biased random walks on the graph. However, node2vec does not consider edge weights when computing walk biases. This intrinsic limitation prevents node2vec from leveraging all the information in weighted graphs and, in turn, limits its application to many real-world networks that are weighted and dense. Here, we naturally extend node2vec to node2vec+ in a way that accounts for edge weights when calculating walk biases, but which reduces to node2vec in the cases of unweighted graphs or unbiased walks. We empirically show that node2vec+ is more robust to additive noise than node2vec in weighted graphs using two synthetic datasets. We also demonstrate that node2vec+ significantly outperforms node2vec on a commonly benchmarked multi-label dataset (Wikipedia). Furthermore, we test node2vec+ against GCN and GraphSAGE using various challenging gene classification tasks on two protein-protein interaction networks. Despite some clear advantages of GCN and GraphSAGE, they show comparable performance with node2vec+. Finally, node2vec+ can be used as a general approach for generating biased random walks, benefiting all existing methods built on top of node2vec. Node2vec+ is implemented as part of , which is available at https://github.com/krishnanlab/PecanPy .

READ FULL TEXT

page 4

page 7

page 11

research
08/11/2023

Node Embedding for Homophilous Graphs with ARGEW: Augmentation of Random walks by Graph Edge Weights

Representing nodes in a network as dense vectors node embeddings is impo...
research
05/19/2020

Learning Representations using Spectral-Biased Random Walks on Graphs

Several state-of-the-art neural graph embedding methods are based on sho...
research
10/22/2019

Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification

In this work, we study semi-supervised multi-label node classification p...
research
09/07/2018

edge2vec: Learning Node Representation Using Edge Semantics

Representation learning for networks provides a new way to mine graphs. ...
research
03/05/2021

Nishimori meets Bethe: a spectral method for node classification in sparse weighted graphs

This article unveils a new relation between the Nishimori temperature pa...
research
01/22/2022

BiasedWalk: Learning Global-aware Node Embeddings via Biased Sampling

Popular node embedding methods such as DeepWalk follow the paradigm of p...
research
05/09/2016

On the Emergence of Shortest Paths by Reinforced Random Walks

The co-evolution between network structure and functional performance is...

Please sign up or login with your details

Forgot password? Click here to reset