4 citations · 16 across the 13 of their papers we have counts for
5 papers · 1 filter
Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks
Anders Aamand, Justin Y. Chen, Piotr Indyk +5
Recent work shows that the expressive power of Graph Neural Networks (GNNs) in distinguishing non-isomorphic graphs is exactly the same as that of the Weisfeiler-Lehman (WL) graph…
Streaming Algorithms for Support-Aware Histograms
Justin Y. Chen, Piotr Indyk, Tal Wagner
Histograms, i.e., piece-wise constant approximations, are a popular tool used to represent data distributions. Traditionally, the difference between the histogram and the underlyin…
Faster Fundamental Graph Algorithms via Learned Predictions
Justin Y. Chen, Sandeep Silwal, Ali Vakilian +1
We consider the question of speeding up classic graph algorithms with machine-learned predictions. In this model, algorithms are furnished with extra advice learned from past or si…
All-Pairs Shortest Path Distances with Differential Privacy: Improved Algorithms for Bounded and Unbounded Weights
Justin Y. Chen, Shyam Narayanan, Yinzhan Xu
We revisit the problem of privately releasing the all-pairs shortest path distances of a weighted undirected graph up to low additive error, which was first studied by Sealfon [Sea…
Triangle and Four Cycle Counting with Predictions in Graph Streams
Justin Y. Chen, Talya Eden, Piotr Indyk +7
We propose data-driven one-pass streaming algorithms for estimating the number of triangles and four cycles, two fundamental problems in graph analytics that are widely studied in…