59 citations · 235 across the 25 of their papers we have counts for
10 papers · 1 filter
Deep Graph Similarity Learning: A Survey
Guixiang Ma, Nesreen K. Ahmed, Theodore L. Willke +1
In many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, su…
Temporal Network Sampling
Nesreen K. Ahmed, Nick Duffield, Ryan A. Rossi
Temporal networks representing a stream of timestamped edges are seemingly ubiquitous in the real-world. However, the massive size and continuous nature of these networks make them…
A View on Deep Reinforcement Learning in System Optimization
Ameer Haj-Ali, Nesreen K. Ahmed, Ted Willke +3
Many real-world systems problems require reasoning about the long term consequences of actions taken to configure and manage the system. These problems with delayed and often seque…
NeuroVectorizer: End-to-End Vectorization with Deep Reinforcement Learning
Ameer Haj-Ali, Nesreen K. Ahmed, Ted Willke +3
One of the key challenges arising when compilers vectorize loops for today's SIMD-compatible architectures is to decide if vectorization or interleaving is beneficial. Then, the co…
On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications
Ryan A. Rossi, Di Jin, Sungchul Kim +3
Structural roles define sets of structurally similar nodes that are more similar to nodes inside the set than outside, whereas communities define sets of nodes with more connection…
Adaptive Shrinkage Estimation for Streaming Graphs
Nesreen K. Ahmed, Nick Duffield
Networks are a natural representation of complex systems across the sciences, and higher-order dependencies are central to the understanding and modeling of these systems. However,…