Unsupervised and Scalable Algorithm for Learning Node Representations

By Tiago Pimentel, Adriano Veloso, Nivio Ziviani. 2017

20 citations recorded by the source corpus.

How researchers see this paper

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

1. Vincent Wenchen Zheng
2. Benedek Rozemberczki
3. Kevin Chen-Chuan Chang
4. Hongyun Cai
5. Rik Sarkar
6. Ryan Davies
7. Charles Sutton
8. Yu Zheng

Citations in the corpus

• A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, background): “(11)) DDRW [47] truncated random walk 2nd + class identity TriDNR [73] truncated random walk 2nd (among node, word & label) node2vec [28] BFS + DFS 2nd UPP-SNE [113] truncated random walk 2nd (user-user & profile-profile) SkipGram with Planetoid [62] sampling node pairs by labels and structure 2nd +”
• GEMSEC: Graph Embedding with Self Clustering (2018, background): “The representation vector specific to node v is f ( v ) and the optimization problem is given by: min This optimization is subject to two standard assumptions [8].”