Work

node2vec: Scalable Feature Learning for Networks

Year: 2016

Type: conference-paper

Abstract: over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.

Source: PubMed

DOI: 10.48550/arxiv.1607.00653

Authors: Grover, Aditya, Leskovec, Jure

Institution: Stanford University

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green