Publications (47)
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods
Md. Khaledur Rahman, Ariful Azad
Graph representation learning is a fast-growing field where one of the main objectives is to generate meaningful representations of graphs in lower-dimensional spaces. The learned…
Publication Trends in Artificial Intelligence Conferences: The Rise of Super Prolific Authors
Ariful Azad, Afeefa Banu
Papers published in top conferences contribute influential discoveries that are reshaping the landscape of modern Artificial Intelligence (AI). We analyzed 87,137 papers from 11 AI…
Parallel Algorithms for Median Consensus Clustering in Complex Networks
Md Taufique Hussain, Mahantesh Halappanavar, Samrat Chatterjee +3
We develop an algorithm that finds the consensus of many different clustering solutions of a graph. We formulate the problem as a median set partitioning problem and propose a gree…
Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN
Tingyi Wanyan, Chenwei Zhang, Ariful Azad +3
We present a multi-filtering Graph Convolution Neural Network (GCN) framework for network embedding task. It uses multiple local GCN filters to do feature extraction in every propa…
FDN: Interpretable Spatiotemporal Forecasting with Future Decomposition Networks
Nicholas Majeske, Ariful Azad
Spatiotemporal systems comprise a collection of spatially distributed yet interdependent entities each generating unique dynamic signals. Highly sophisticated methods have been pro…
Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation
Penporn Koanantakool, Alnur Ali, Ariful Azad +5
Across a variety of scientific disciplines, sparse inverse covariance estimation is a popular tool for capturing the underlying dependency relationships in multivariate data. Unfor…
The Parallelism Motifs of Genomic Data Analysis
Katherine Yelick, Aydin Buluc, Muaaz Awan +11
Genomic data sets are growing dramatically as the cost of sequencing continues to decline and small sequencing devices become available. Enormous community databases store and shar…
The Reverse Cuthill-McKee Algorithm in Distributed-Memory
Ariful Azad, Mathias Jacquelin, Aydin Buluc +1
Ordering vertices of a graph is key to minimize fill-in and data structure size in sparse direct solvers, maximize locality in iterative solvers, and improve performance in graph a…
Distributed Many-to-Many Protein Sequence Alignment using Sparse Matrices
Oguz Selvitopi, Saliya Ekanayake, Giulia Guidi +3
Identifying similar protein sequences is a core step in many computational biology pipelines such as detection of homologous protein sequences, generation of similarity protein gra…
Triple Sparsification of Graph Convolutional Networks without Sacrificing the Accuracy
Md. Khaledur Rahman, Ariful Azad
Graph Neural Networks (GNNs) are widely used to perform different machine learning tasks on graphs. As the size of the graphs grows, and the GNNs get deeper, training and inference…
Distributed-Memory Parallel Algorithms for Sparse Matrix and Sparse Tall-and-Skinny Matrix Multiplication
Isuru Ranawaka, Md Taufique Hussain, Charles Block +3
We consider a sparse matrix-matrix multiplication (SpGEMM) setting where one matrix is square and the other is tall and skinny. This special variant, called TS-SpGEMM, has importan…
Parallel Algorithms for Adding a Collection of Sparse Matrices
Md Taufique Hussain, Guttu Sai Abhishek, Aydin Buluç +1
We develop a family of parallel algorithms for the SpKAdd operation that adds a collection of k sparse matrices. SpKAdd is a much needed operation in many applications including di…
A Scalable Method for Readable Tree Layouts
Kathryn Gray, Mingwei Li, Reyan Ahmed +4
Large tree structures are ubiquitous and real-world relational datasets often have information associated with nodes (e.g., labels or other attributes) and edges (e.g., weights or…
Resampling Residuals: Robust Estimators of Error and Fit for Evolutionary Trees and Phylogenomics
Peter J. Waddell, Ariful Azad
Phylogenomics, even more so than traditional phylogenetics, needs to represent the uncertainty in evolutionary trees due to systematic error. Here we illustrate the analysis of gen…
Sparsity-Aware Roofline Models for Sparse Matrix-Matrix Multiplication
Matthew Qian, Yahia Ramadan, Suhita Anubha +1
Sparse matrix-dense matrix multiplication (SpMM) is a critical kernel in scientific computing, graph analytics, and machine learning, whose performance is often constrained by memo…
DistShap: Scalable GNN Explanations with Distributed Shapley Values
Selahattin Akkas, Aditya Devarakonda, Ariful Azad
With the growing adoption of graph neural networks (GNNs), explaining their predictions has become increasingly important. However, attributing predictions to specific edges or fea…
Inductive Predictions of Extreme Hydrologic Events in The Wabash River Watershed
Nicholas Majeske, Bidisha Abesh, Chen Zhu +1
We present a machine learning method to predict extreme hydrologic events from spatially and temporally varying hydrological and meteorological data. We used a timestep reduction t…
PLANETALIGN: A Comprehensive Python Library for Benchmarking Network Alignment
Qi Yu, Zhichen Zeng, Yuchen Yan +5
Network alignment (NA) aims to identify node correspondence across different networks and serves as a critical cornerstone behind various downstream multi-network learning tasks. D…
Force2Vec: Parallel force-directed graph embedding
Md. Khaledur Rahman, Majedul Haque Sujon, Ariful Azad
A graph embedding algorithm embeds a graph into a low-dimensional space such that the embedding preserves the inherent properties of the graph. While graph embedding is fundamental…
Shapley-Value-Based Graph Sparsification for GNN Inference
Selahattin Akkas, Ariful Azad
Graph sparsification is a key technique for improving inference efficiency in Graph Neural Networks by removing edges with minimal impact on predictions. GNN explainability methods…
Extreme-scale many-against-many protein similarity search
Oguz Selvitopi, Saliya Ekanayake, Giulia Guidi +7
Similarity search is one of the most fundamental computations that are regularly performed on ever-increasing protein datasets. Scalability is of paramount importance for uncoverin…
A work-efficient parallel sparse matrix-sparse vector multiplication algorithm
Ariful Azad, Aydin Buluc
We design and develop a work-efficient multithreaded algorithm for sparse matrix-sparse vector multiplication (SpMSpV) where the matrix, the input vector, and the output vector are…
A Distributed-Memory Algorithm for Computing a Heavy-Weight Perfect Matching on Bipartite Graphs
Ariful Azad, Aydın Buluc, Xiaoye S. Li +2
We design and implement an efficient parallel algorithm for finding a perfect matching in a weighted bipartite graph such that weights on the edges of the matching are large. This…
FastSV: A Distributed-Memory Connected Component Algorithm with Fast Convergence
Yongzhe Zhang, Ariful Azad, Zhenjiang Hu
This paper presents a new distributed-memory algorithm called FastSV for finding connected components in an undirected graph. Our algorithm simplifies the classic Shiloach-Vishkin…
Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records
Tingyi Wanyan, Hossein Honarvar, Ariful Azad +2
Computational prediction of in-hospital mortality in the setting of an intensive care unit can help clinical practitioners to guide care and make early decisions for interventions.…
Distributed-memory Algorithms for Sparse Matrix Permutation, Extraction, and Assignment
Elaheh Hassani, Md Taufique Hussain, Ariful Azad
We present scalable distributed-memory algorithms for sparse matrix permutation, extraction, and assignment. Our methods follow an Identify-Exchange-Build (IEB) strategy where each…
SparseTransX: Efficient Training of Translation-Based Knowledge Graph Embeddings Using Sparse Matrix Operations
Md Saidul Hoque Anik, Ariful Azad
Knowledge graph (KG) learning offers a powerful framework for generating new knowledge and making inferences. Training KG embedding can take a significantly long time, especially f…
BatchLayout: A Batch-Parallel Force-Directed Graph Layout Algorithm in Shared Memory
Md. Khaledur Rahman, Majedul Haque Sujon, Ariful Azad
Force-directed algorithms are widely used to generate aesthetically pleasing layouts of graphs or networks arisen in many scientific disciplines. To visualize large-scale graphs, s…
Combinatorial BLAS 2.0: Scaling combinatorial algorithms on distributed-memory systems
Ariful Azad, Oguz Selvitopi, Md Taufique Hussain +2
Combinatorial algorithms such as those that arise in graph analysis, modeling of discrete systems, bioinformatics, and chemistry, are often hard to parallelize. The Combinatorial B…
Immunophenotypes of Acute Myeloid Leukemia From Flow Cytometry Data Using Templates
Ariful Azad, Bartek Rajwa, Alex Pothen
Motivation: We investigate whether a template-based classification pipeline could be used to identify immunophenotypes in (and thereby classify) a heterogeneous disease with many s…
Scalable Optimal Transport Algorithm for Network Alignment
Elaheh Hassani, Durga Mandarapu, Qi Yu +2
Network alignment identifies node correspondences across different networks and is a fundamental primitive in many data science applications, including social network analysis, fra…
MarkovGNN: Graph Neural Networks on Markov Diffusion
Md. Khaledur Rahman, Abhigya Agrawal, Ariful Azad
Most real-world networks contain well-defined community structures where nodes are densely connected internally within communities. To learn from these networks, we develop MarkovG…
Bandwidth-Optimized Parallel Algorithms for Sparse Matrix-Matrix Multiplication using Propagation Blocking
Zhixiang Gu, Jose Moreira, David Edelsohn +1
Sparse matrix-matrix multiplication (SpGEMM) is a widely used kernel in various graph, scientific computing and machine learning algorithms. It is well known that SpGEMM is a memor…
iSpLib: A Library for Accelerating Graph Neural Networks using Auto-tuned Sparse Operations
Md Saidul Hoque Anik, Pranav Badhe, Rohit Gampa +1
Core computations in Graph Neural Network (GNN) training and inference are often mapped to sparse matrix operations such as sparse-dense matrix multiplication (SpMM). These sparse…
Integrated Model, Batch and Domain Parallelism in Training Neural Networks
Amir Gholami, Ariful Azad, Peter Jin +2
We propose a new integrated method of exploiting model, batch and domain parallelism for the training of deep neural networks (DNNs) on large distributed-memory computers using min…
Exploiting Multiple Levels of Parallelism in Sparse Matrix-Matrix Multiplication
Ariful Azad, Grey Ballard, Aydin Buluc +5
Sparse matrix-matrix multiplication (or SpGEMM) is a key primitive for many high-performance graph algorithms as well as for some linear solvers, such as algebraic multigrid. The s…
SubGraph2Vec: Highly-Vectorized Tree-likeSubgraph Counting
Langshi Chen, Jiayu Li, Ariful Azad +7
Subgraph counting aims to count occurrences of a template T in a given network G(V, E). It is a powerful graph analysis tool and has found real-world applications in diverse domain…
Resampling Residuals on Phylogenetic Trees: Extended Results
Peter J. Waddell, Ariful Azad, Ishita Khan
In this article the results of Waddell and Azad (2009) are extended. In particular, the geometric percentage mean standard deviation measure of the fit of distances to a phylogenet…
Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data
Tingyi Wanyan, Jing Zhang, Ying Ding +3
Electronic Health Record (EHR) data has been of tremendous utility in Artificial Intelligence (AI) for healthcare such as predicting future clinical events. These tasks, however, o…
Communication-Avoiding and Memory-Constrained Sparse Matrix-Matrix Multiplication at Extreme Scale
Md Taufique Hussain, Oguz Selvitopi, Aydin Buluç +1
Sparse matrix-matrix multiplication (SpGEMM) is a widely used kernel in various graph, scientific computing and machine learning algorithms. In this paper, we consider SpGEMMs perf…
FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks
Md. Khaledur Rahman, Majedul Haque Sujon, Ariful Azad
We develop a fused matrix multiplication kernel that unifies sampled dense-dense matrix multiplication and sparse-dense matrix multiplication under a single operation called FusedM…
Optimizing High Performance Markov Clustering for Pre-Exascale Architectures
Oguz Selvitopi, Md Taufique Hussain, Ariful Azad +1
HipMCL is a high-performance distributed memory implementation of the popular Markov Cluster Algorithm (MCL) and can cluster large-scale networks within hours using a few thousand…
A GraphBLAS Approach for Subgraph Counting
Langshi Chen, Jiayu Li, Ariful Azad +7
Subgraph counting aims to count the occurrences of a subgraph template T in a given network G. The basic problem of computing structural properties such as counting triangles and o…
An Algorithmic Pipeline for Analyzing Multi-parametric Flow Cytometry Data
Ariful Azad
Flow cytometry (FC) is a single-cell profiling platform for measuring the phenotypes of individual cells from millions of cells in biological samples. FC employs high-throughput te…
GNNShap: Scalable and Accurate GNN Explanation using Shapley Values
Selahattin Akkas, Ariful Azad
Graph neural networks (GNNs) are popular machine learning models for graphs with many applications across scientific domains. However, GNNs are considered black box models, and it…
Graphical Games for UAV Swarm Control Under Time-Varying Communication Networks
Malintha Fernando, Ransalu Senanayake, Ariful Azad +1
We propose a unified framework for coordinating Unmanned Aerial Vehicle (UAV) swarms operating under time-varying communication networks. Our framework builds on the concept of gra…
High-performance sparse matrix-matrix products on Intel KNL and multicore architectures
Yusuke Nagasaka, Satoshi Matsuoka, Ariful Azad +1
Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is widely used in areas ranging from traditional numerical applications to recent big data analysis a…