Nesreen K. Ahmed

research

∙ 09/02/2023

Bias and Fairness in Large Language Models: A Survey

Rapid advancements of large language models (LLMs) have enabled the proc...

0 Isabel O. Gallegos, et al. ∙

research

∙ 07/08/2023

Fairness-Aware Graph Neural Networks: A Survey

Graph Neural Networks (GNNs) have become increasingly important due to t...

0 April Chen, et al. ∙

research

∙ 05/09/2023

Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representation

Detecting parallelizable code regions is a challenging task, even for ex...

0 Le Chen, et al. ∙

research

∙ 03/07/2023

Neural Compositional Rule Learning for Knowledge Graph Reasoning

Learning logical rules is critical to improving reasoning in KGs. This i...

0 Kewei Cheng, et al. ∙

research

∙ 12/22/2022

Graph Learning with Localized Neighborhood Fairness

Learning fair graph representations for downstream applications is becom...

0 April Chen, et al. ∙

research

∙ 09/22/2022

Memory-Augmented Graph Neural Networks: A Neuroscience Perspective

Graph neural networks (GNNs) have been extensively used for many domains...

0 Guixiang Ma, et al. ∙

research

∙ 04/25/2022

End-to-end Mapping in Heterogeneous Systems Using Graph Representation Learning

To enable heterogeneous computing systems with autonomous programming an...

0 Yao Xiao, et al. ∙

research

∙ 01/22/2022

Joint Learning of Hierarchical Community Structure and Node Representations: An Unsupervised Approach

Graph representation learning has demonstrated improved performance in t...

0 Ancy Sarah Tom, et al. ∙

research

∙ 04/14/2021

DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks

Full-batch training on Graph Neural Networks (GNN) to learn the structur...

29 Vasimuddin Md, et al. ∙

research

∙ 02/12/2021

Personalized Visualization Recommendation

Visualization recommendation work has focused solely on scoring visualiz...

0 Xin Qian, et al. ∙

research

∙ 10/23/2020

Heterogeneous Graphlets

In this paper, we introduce a generalization of graphlets to heterogeneo...

0 Ryan A. Rossi, et al. ∙

research

∙ 10/13/2020

PIUMA: Programmable Integrated Unified Memory Architecture

High performance large scale graph analytics is essential to timely anal...

0 Sriram Aananthakrishnan, et al. ∙

research

∙ 10/09/2020

A Vertex Cut based Framework for Load Balancing and Parallelism Optimization in Multi-core Systems

High-level applications, such as machine learning, are evolving from sim...

0 Guixiang Ma, et al. ∙

research

∙ 09/28/2020

Graph Neural Networks with Heterophily

Graph Neural Networks (GNNs) have proven to be useful for many different...

30 Jiong Zhu, et al. ∙

research

∙ 12/25/2019

Deep Graph Similarity Learning: A Survey

In many domains where data are represented as graphs, learning a similar...

25 Guixiang Ma, et al. ∙

research

∙ 10/18/2019

Temporal Network Sampling

Temporal networks representing a stream of timestamped edges are seeming...

21 Nesreen K. Ahmed, et al. ∙

research

∙ 09/20/2019

NeuroVectorizer: End-to-End Vectorization with Deep Reinforcement Learning

One of the key challenges arising when compilers vectorize loops for tod...

0 Ameer Haj-Ali, et al. ∙

research

∙ 08/22/2019

From Community to Role-based Graph Embeddings

Roles are sets of structurally similar nodes that are more similar to no...

0 Ryan A. Rossi, et al. ∙

research

∙ 08/04/2019

Deep Reinforcement Learning in System Optimization

The recent advancements in deep reinforcement learning have opened new h...

6 Ameer Haj-Ali, et al. ∙

research

∙ 08/02/2019

Network Shrinkage Estimation

Networks are a natural representation of complex systems across the scie...

3 Nesreen K. Ahmed, et al. ∙

research

∙ 06/14/2019

Linear-time Hierarchical Community Detection

Community detection in graphs has many important and fundamental applica...

0 Ryan A. Rossi, et al. ∙

research

∙ 06/12/2019

Higher-Order Ranking and Link Prediction: From Closing Triangles to Closing Higher-Order Motifs

In this paper, we introduce the notion of motif closure and describe hig...

0 Ryan A. Rossi, et al. ∙

research

∙ 04/12/2019

Temporal Network Representation Learning

Networks evolve continuously over time with the addition, deletion, and ...

22 John Boaz Lee, et al. ∙

research

∙ 01/28/2019

Heterogeneous Network Motifs

Many real-world applications give rise to large heterogeneous networks w...

0 Ryan A. Rossi, et al. ∙

research

∙ 11/02/2018

Similarity Learning with Higher-Order Proximity for Brain Network Analysis

In recent years, the similarity learning problem has been widely studied...

0 Guixiang Ma, et al. ∙

research

∙ 07/20/2018

Attention Models in Graphs: A Survey

Graph-structured data arise naturally in many different application doma...

0 John Boaz Lee, et al. ∙

research

∙ 05/07/2018

Predicting Graph Categories from Structural Properties

Complex networks are often categorized according to the underlying pheno...

4 James P. Canning, et al. ∙

research

∙ 02/07/2018

Learning Role-based Graph Embeddings

Random walks are at the heart of many existing network embedding methods...

0 Nesreen K. Ahmed, et al. ∙

research

∙ 01/28/2018

HONE: Higher-Order Network Embeddings

This paper describes a general framework for learning Higher-Order Netwo...

0 Ryan A. Rossi, et al. ∙

research

∙ 12/22/2017

Estimating Node Similarity by Sampling Streaming Bipartite Graphs

Bipartite graph data increasingly occurs as a stream of edges that repre...

0 Nesreen K. Ahmed, et al. ∙

research

∙ 10/27/2017

Similarity-based Multi-label Learning

Multi-label classification is an important learning problem with many ap...

0 Ryan A. Rossi, et al. ∙

research

∙ 10/25/2017

Inductive Representation Learning in Large Attributed Graphs

Graphs (networks) are ubiquitous and allow us to model entities (nodes) ...

0 Nesreen K. Ahmed, et al. ∙

research

∙ 09/14/2017

A Framework for Generalizing Graph-based Representation Learning Methods

Random walks are at the heart of many existing deep learning algorithms ...

0 Nesreen K. Ahmed, et al. ∙

research

∙ 09/13/2017

Network Classification and Categorization

To the best of our knowledge, this paper presents the first large-scale ...

0 James P. Canning, et al. ∙

research

∙ 04/28/2017

Deep Feature Learning for Graphs

This paper presents a general graph representation learning framework ca...

0 Ryan A. Rossi, et al. ∙

research

∙ 01/06/2017

Estimation of Graphlet Statistics

Graphlets are induced subgraphs of a large network and are important for...

0 Ryan A. Rossi, et al. ∙

research

∙ 10/04/2016

Revisiting Role Discovery in Networks: From Node to Edge Roles

Previous work in network analysis has focused on modeling the mixed-memb...

0 Nesreen K. Ahmed, et al. ∙

research

∙ 08/02/2016

Relational Similarity Machines

This paper proposes Relational Similarity Machines (RSM): a fast, accura...

0 Ryan A. Rossi, et al. ∙

research

∙ 06/13/2015

Graphlet Decomposition: Framework, Algorithms, and Applications

From social science to biology, numerous applications often rely on grap...

0 Nesreen K. Ahmed, et al. ∙

research

∙ 02/02/2015

A Web-based Interactive Visual Graph Analytics Platform

This paper proposes a web-based visual graph analytics platform for inte...

0 Nesreen K. Ahmed, et al. ∙

research

∙ 03/14/2014

Learning the Latent State Space of Time-Varying Graphs

From social networks to Internet applications, a wide variety of electro...

0 Nesreen K. Ahmed, et al. ∙

research

∙ 11/14/2012

Network Sampling: From Static to Streaming Graphs

Network sampling is integral to the analysis of social, information, and...

0 Nesreen K. Ahmed, et al. ∙

Nesreen K. Ahmed

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