Personalized PageRank (PPR) is a traditional measure for node proximity on large graphs.
For a pair of nodes and, the PPR value equals the probability that an-discounted random …

Abstract Graph Neural Networks (GNNs) have proven to be highly effective in solving real-
world learning problems that involve graph-structured data. However, GNNs can also …

Social event detection refers to extracting relevant message clusters from social media data
streams to represent specific events in the real world. Social event detection is important in …

Graph diffusion, which iteratively propagates real-valued substances among the graph, is
used in numerous graph/network-involved applications. However, releasing diffusion …

Hierarchical Clustering is a popular unsupervised machine learning method with decades of
history and numerous applications. We initiate the study of differentially-private …

Personalized PageRank Vectors are widely used as fundamental graph-learning tools for
detecting anomalous spammers, learning graph embeddings, and training graph neural …

We describe a new setting for learning a deep graph generative model (GGM) from
aggregate graph statistics, rather than from the graph adjacency matrix. Matching the …

As the field of Graph Neural Networks (GNN) continues to grow, it experiences a
corresponding increase in the need for large, real-world datasets to train and test new GNN …

When designing interventions in public health, development, and education, decision
makers rely on social network data to target a small number of people, capitalizing on peer …

Graph Neural Networks (GNNs) have proven to be highly effective in addressing the link
prediction problem. However, the need for large amounts of user data to learn …