It's who you know: graph mining using recursive structural features

By Keith W. Henderson, Brian Gallagher, Lei Li, Leman Akoglu, Tina Eliassi-Rad, Hanghang Tong, Christos Faloutsos. 2011

282 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Statistical and Nonlinear Physics, Graph Learning

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. Jure Leskovec
2. Bryan Perozzi
3. Jun Zhu
4. William L. Hamilton
5. Danai Koutra
6. Hanghang Tong
7. Stephan Günnemann
8. Leman Akoglu

In Management Science and Operations Research

1. Rami Al-Rfou
2. Tina Eliassi-Rad
3. Lei Li
4. Jing Gao
5. Jun Zhu
6. Aidong Zhang
7. Brian Gallagher
8. Jure Leskovec

In Statistical and Nonlinear Physics

1. Jure Leskovec
2. Bryan Perozzi
3. Hanghang Tong
4. Danai Koutra
5. Leman Akoglu
6. Christos Faloutsos
7. Jun Zhu
8. Juzheng Li

In Graph Learning

1. Jure Leskovec
2. Bryan Perozzi
3. William L. Hamilton
4. Stephan Günnemann
5. Jun Zhu
6. Aleksandar Bojchevski
7. Aditya Grover
8. Leman Akoglu

Citations in the corpus

• node2vec: Scalable Feature Learning for Networks (2016, background): “In networks, the conventional paradigm for generating features for nodes is based on feature extraction techniques which typically involve some seed hand-crafted features based on network properties [8, 11].”
• DeepWalk: online learning of social representations (2014, methodology): “Most methods require global information and are offline [17,39–41].”
• LRBM: A Restricted Boltzmann Machine Based Approach for Representation Learning on Linked Data (2014, background): “Knowledge discovery on linked data is the process of leveraging both node attributes and link structures for the learning on the corresponding systems, and it is of great importance to understanding the characteristics and interaction patterns in these systems.”
• RolX: structural role extraction & mining in large graphs (2012, methodology): “Thus, unless otherwise stated RolX employs the feature extraction algorithm described in [15], non-negative matrix factorization for clustering, MDL for model selection, and KL divergence to measure likelihood.”
• Discriminative Deep Random Walk for Network Classification (2016, methodology): “(Henderson et al., 2011) propose a regional feature generating method and demonstrate the usage of the regional feature in within-network and across-network classification.”
• Combining Label Propagation and Simple Models Out-performs Graph Neural Networks (2020, background): “There are numerous ways to get useful features from just the graph topology to augment the raw node features (Henderson et al., 2011; 2012; Hamilton et al., 2017b).”
• Learning Structural Node Embeddings via Diffusion Wavelets (2017, unknown): “A notable example of such approaches is RolX [11, 16], which aims to recover a soft-clustering of nodes into a predetermined number of K distinct roles using recursive feature extraction [17].”