270 citations · 805 across the 13 of their papers we have counts for
6 papers · 1 filter
Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs
Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi
Graph comparison is a fundamental operation in data mining and information retrieval. Due to the combinatorial nature of graphs, it is hard to balance the expressiveness of the sim…
Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts
Alessandro Epasto, Bryan Perozzi
Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph. But can nodes really be best described by a single vector rep…
Enhanced Network Embeddings via Exploiting Edge Labels
Haochen Chen, Xiaofei Sun, Yingtao Tian +3
Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. While achieving competitive performance on a variety of network inference tas…
A Tutorial on Network Embeddings
Haochen Chen, Bryan Perozzi, Rami Al-Rfou +1
Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. These representations can be used as features for a wide range of tasks on gr…
HARP: Hierarchical Representation Learning for Networks
Haochen Chen, Bryan Perozzi, Yifan Hu +1
We present HARP, a novel method for learning low dimensional embeddings of a graph's nodes which preserves higher-order structural features. Our proposed method achieves this by co…
Ties That Bind - Characterizing Classes by Attributes and Social Ties
Aria Rezaei, Bryan Perozzi, Leman Akoglu
Given a set of attributed subgraphs known to be from different classes, how can we discover their differences? There are many cases where collections of subgraphs may be contrasted…