Graphs are a representation of structured data that captures the
relatio...
Despite a surge in interest in GNN development, homogeneity in benchmark...
Graph Neural Networks (GNNs) have shown remarkable performance on
graph-...
Unsupervised learning has recently significantly gained in popularity,
e...
Personalized PageRank (PPR) is a fundamental tool in unsupervised learni...
A surge of interest in Graph Convolutional Networks (GCN) has produced
t...
Representative selection (RS) is the problem of finding a small subset o...
Graph learning algorithms have attained state-of-the-art performance on ...
How can we make predictions for nodes in a heterogeneous graph when an e...
Despite advances in the field of Graph Neural Networks (GNNs), only a sm...
There has been a recent surge of interest in designing Graph Neural Netw...
Graph Representation Learning (GRL) methods have impacted fields from
ch...
In this work we propose Pathfinder Discovery Networks (PDNs), a method f...
In this paper, we introduce InstantEmbedding, an efficient method for
ge...
How can we find the right graph for semi-supervised learning? In real wo...
In this work, we examine a novel forecasting approach for COVID-19 case
...
Graph neural networks (GNNs) have emerged as a powerful approach for sol...
Graph Neural Networks (GNNs) have achieved state-of-the-art results on m...
There has been a surge of recent interest in learning representations fo...
Graph comparison is a fundamental operation in data mining and informati...
Are Graph Neural Networks (GNNs) fair? In many real world graphs, the
fo...
Recent interest in graph embedding methods has focused on learning a sin...
Existing popular methods for semi-supervised learning with Graph Neural
...
Can neural networks learn to compare graphs without feature engineering?...
Graph Convolutional Networks (GCNs) have shown significant improvements ...
We present ASYMP, a distributed graph processing system developed for th...
Graph embedding methods represent nodes in a continuous vector space,
pr...
We propose a new method for embedding graphs while preserving directed e...
Do word embeddings converge to learn similar things over different
initi...
We present a new computational technique to detect and analyze statistic...
We seek to better understand the difference in quality of the several
pu...