Structural Neighborhood Based Classification of Nodes in a Network

By Sharad Nandanwar, M. Murty. 2016

56 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Stephan Günnemann
2. Aleksandar Bojchevski
3. Johannes Klicpera
4. Eliav Buchnik
5. Edith Cohen
6. Daniel Zügner
7. William L. Hamilton
8. Pan Li

In Statistical and Nonlinear Physics

1. Stephan Günnemann
2. Johannes Klicpera
3. Aleksandar Bojchevski
4. Eliav Buchnik
5. Edith Cohen
6. Daniel Zügner
7. Matthias Fey
8. Muhan Zhang

Citations in the corpus

• Predict then Propagate: Graph Neural Networks meet Personalized PageRank (2018, background): “…walks or matrix factorization to directly train individual node embeddings, often without using node features and usually in an unsupervised manner, i.e. without leveraging node classes (Perozzi et al., 2014; Tang et al., 2015; Nandanwar & Murty, 2016; Grover & Leskovec, 2016; Qiu et al., 2018).”
• Bootstrapped Graph Diffusions (2018, background): “This abundance of recent work introduced many new components and often at the same time: Low-rank embeddings, non-linear propagations, learning of weights of hidden layers (GCNs), learning of node weights [29].”
• Bootstrapped Graph Diffusions: Exposing the Power of Nonlinearity (2017, methodology): “Moreover, since the self-training wrapper retains the scalability of the base method, we obtain both higher quality and be er scalability.”
• Bootstrapped Graph Diffusions: Exposing the Power of Nonlinearity (2018, background): “This abundance of recent work introduced many new components and often at the same time: Low-rank embeddings, non-linear propagations, learning of weights of hidden layers (GCNs), learning of node weights [28].”