270 citations · 565 across the 6 of their papers we have counts for
7 papers
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor +5
Existing popular methods for semi-supervised learning with Graph Neural Networks (such as the Graph Convolutional Network) provably cannot learn a general class of neighborhood mix…
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…
DDGK: Learning Graph Representations for Deep Divergence Graph Kernels
Rami Al-Rfou, Dustin Zelle, Bryan Perozzi
Can neural networks learn to compare graphs without feature engineering? In this paper, we show that it is possible to learn representations for graph similarity with neither domai…
ASYMP: Fault-tolerant Mining of Massive Graphs
Eduardo Fleury, Silvio Lattanzi, Vahab Mirrokni +1
We present ASYMP, a distributed graph processing system developed for the timely analysis of graphs with trillions of edges. ASYMP has several distinguishing features including a r…
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…