Paper-Conference

Community detection plays a central role in uncovering meso scale structures in networks. However, existing methods often suffer from …

Large Language Models (LLMs) are increasingly used to automate software development, yet most prior evaluations focus on functional …

Subgraph isomorphism algorithms face significant scalability bottlenecks on large-scale property graphs due to inefficient …

The rise of graph data in various fields calls for efficient and scalable community detection algorithms. In this paper, we present …

Connectomics, a subfield of neuroscience, reconstructs structural and functional brain maps at synapse-level resolution. These complex …

Understanding information flow in the brain can be facilitated by arranging neurons in the fly connectome to form a maximally …

For fast processing of increasingly large graphs, triangle counting - a common building block of graph processing algorithms, is often …

Data sets have grown exponentially in size, rapidly surpassing the scale at which traditional exploratory data analysis (EDA) tools can …

This paper introduces an innovative design for Enhanced Knowledge Graph Attention Networks (EKGAT), focusing on improving …

This paper introduces a novel, parallel, and scalable implementation of the VF2 algorithm for subgraph monomorphism developed in the …

This research presents an enhanced Graph Attention Convolutional Neural Network (GAT) tailored for the analysis of open-source package …

Finding connected components in a graph is a fundamental problem in graph analysis. In this work, we present a novel minimum-mapping …

Listing and counting triangles in graphs is a key algorithmic kernel for network analyses including community detection, clustering …

Counting and finding triangles in graphs is often used in real-world analytics to characterize cohesiveness and identify communities in …

Analyzing large-scale graphs poses challenges due to their increasing size and the demand for interactive and user-friendly analytics …

One of the most critical problems in the field of string algorithms is the longest common subsequence problem (LCS). The problem is …

Triangles and squares count are widely-used graph analytic metrics providing insights into the connectivity of a graph. While the …

Finding connected components is a fundamental problem in graph analysis. We develop a novel minimum- mapping based Contour algorithm to …

This paper presents an algorithm for detecting attributed high-degree node isomorphism. High-degree isomorphic nodes seldom happen by …

In graph analytics, the Truss is a cohesive subgraph based on the number of triangles supporting each edge. It is widely used for …

The suffix array is a fundamental data structure to support string analysis efficiently. It took about 26 years for the sequential …

There are a wide number of graph centrality metrics. Further, the performance of each can vary widely depending on the type of …

Due to the emergence of massive real-world graphs, whose sizes may extend to terabytes, new tools must be developed to enable data …

The K-Truss of a graph is a cohesive subgraph that has been widely used for community detection in applications such as social networks …

The transitive closure of a graph is a new graph where every vertex is directly connected to all vertices to which it had a path in the …

Large scale data sets from the web, social networks, and bioinformatics are widely available and can often be represented by strings …

Exploratory graph analytics helps maximize the informational value from a graph. However, the increasing graph size makes it impossible …

Identifying key members in large social network graphs is an important graph analytic. Recently, a new centrality measure called …

K-Truss Implementation in Arkouda, a data science framework for graph analytics. Arkouda is an open source framework.

Exploratory graph analytics helps maximize the informational value for a graph. However, the increasing graph size makes it impossible …

Graph algorithms can be expressed in terms of linear algebra. GraphBLAS is a library of low-level building blocks for such algorithms …

Large fault-tolerant network systems with high Quality of Service (QoS) guarantee are critical in many real world applications and …

Identifying anomalies in millions of stars in real time is a great challenge. In this paper, we develop a matched filtering based …

This Scale free networks are abundant in many natural, social, and engineering phenomena for which there exists a substantial corpus of …

The lines between data science (DS), machine learning (ML), deep learning (DL), and data mining continue to be blurred and removed. …

Graph processing is typically considered to be a memory-bound rather than compute-bound problem. One common line of thought is that …

Counting common neighbors between all vertex pairs in a graph is a fundamental operation, with uses in similarity measures, link …

The constant and massive influx of new data into analysis systems needs to be addressed without assuming we can pause the onslaught. …

List intersections are ubiquitous and can be found in a wide range of applications, including triangle counting and finding the maximal …

Sparse data computations are ubiquitous in science and engineering. Unlike their dense data counterparts, sparse data computations have …

Presents the introductory welcome message from the conference proceedings. May include the conference officers’ congratulations …

Triangle counting is a building block for numerous graph applications and given the fact that graphs continue to grow in size, its …

Emerging real-world graph problems include: detecting and preventing disease in human populations; revealing community structure in …

Many large datasets from several fields of research such as biology or society can be represented as graphs. Additionally in many real …

Network analysis defines a number of centrality measures to identify the most central nodes in a network. Fast computation of those …

Many large datasets from a variety of fields of research can be represented as graphs. A common query is to identify the most …

Dynamic graphs can capture changing relationships in many real datasets that evolve over time. One of the most basic questions about …

PageRank is a fundamental graph algorithm to evaluate the importance of vertices in a graph. In this paper, we present an efficient …

Triangle counting is an important building block for finding key players in a graph. It is an integral part of the popular clustering …

Presents the introductory welcome message from the conference proceedings. May include the conference officers’ congratulations …

The k-truss of a graph is a subgraph such that each edge is tightly connected to the remaining elements in the k-truss. The k-truss of …

Many graph datasets originating from online social network, financial or biological sources are too large to store or analyze. The …

Power is a primary concern for mobile, cloud, and high-performance computing applications. Approximate computing refers to running …

In this work we present a new local, vertex-level measure of community change. Our measure detects vertices that change community …

This paper details a distributed memory implementation of Reptile, a scalable and accurate spectrum based error-correction method. …

Dynamic graphs are used to represent changing relational data. In order to create a dynamic graph representing relationships or …

cuSTINGER, a new graph data structure targeting NVIDIA GPUs is designed for streaming graphs that evolve over time. cuSTINGER enables …

Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the …

Provides a listing of current committee members and society officers.

The GraphBLAS standard (GraphBlas.org) is being developed to bring the potential of matrix-based graph algorithms to the broadest …

Spectral partitioning (clustering) algorithms use eigenvectors to solve network analysis problems. The relationship between numerical …

In 2013 a paper was offered to the CAA concerning archaeological legacy data and semantic database applications, with some preliminary …

A variety of massive datasets, such as social networks and biological data, are represented as graphs that reveal underlying …

Optimized GPU kernels are sufficiently complicated to write that they often are specialized to input data, target architectures, or …

As multicore processor architectures are now prevalent in server nodes of parallel and distributed computing systems, it has become …

The construction of efficient parallel graph algorithms is important for quickly solving problems in areas such as urban planning, …

Community detection, or graph clustering, is the problem of finding dense groups in a graph. This is important for a variety of …

Provides a listing of current committee members and society officers. The conference also offers a note of thanks and lists its …

Contemporary microprocessors use relaxed memory consistency models to allow for aggressive optimizations in hardware. This enhancement …

Keynote Abstracts

High-performance graph analysis is unlocking knowledge in problems like anomaly detection in computer security, community structure in …

In this paper, we designed a distance metric as DCJ-Indel-Exemplar distance to estimate the dissimilarity between two genomes with …

With the proliferation of large, irregular, and sparse relational datasets, new storage and analysis platforms have arisen to fill gaps …

Breadth-First Search (BFS) is widely used in real-world applications including computational biology, social networks, and electronic …

The Basic Linear Algebra Subprograms (BLAS), introduced over 30 years ago, had a transformative effect on linear algebra. By building …

Provides a listing of current committee members and society officers.

Clustering coefficients is a building block in network sciences that offers insights on how tightly bound vertices are in a network. …

Applications of high-performance graph analysis range from computational biology to network security and even transportation. These …

Betweenness Centrality is a widely used graph analytic that has applications such as finding influential people in social networks, …

Graphs that model social networks, numerical simulations, and the structure of the Internet are enormous and cannot be manually …

Social networks, communication networks, business intelligence databases, and large scientific data sources now contain hundreds of …

In this paper we propose a new methodology for gaining insight into the temporal aspects of social networks. In order to develop …

Turning large volumes of data into actionable knowledge is a top challenge in high performance computing. Our previous work in this …

This paper reports on methods and results of an applied research project by a team consisting of SAIC and four universities to develop, …

High response quality is critical for many best-effort interactive services, and at the same time, reducing energy consumption can …

Clustering coefficients, also called triangle counting, is a widely-used graph analytic for measuring the closeness in which vertices …

Presents the introductory welcome message from the conference proceedings.

Implementing parallel graph algorithms in large, shared memory machines, such as the Cray XMT, can be challenging for programmers. …

The increasing energy consumption of high performance computing has resulted in rising operational and environmental costs. Therefore, …

Analyzing static snapshots of massive, graph-structured data cannot keep pace with the growth of social networks, financial …

It is our view that the state of the art in constructing a large collection of graph algorithms in terms of linear algebraic operations …

Analysis of social networks is challenging due to the rapid changes of its members and their relationships. For many cases it …

Analyzing static snapshots of massive, graph-structured data cannot keep pace with the growth of social networks, financial …

One of the key challenges in advanced micro-architecture is to provide high performance hardware-components that work as application …

Graphics Processing Units (GPUs) have become ideal candidates for the development of fine-grain parallel algorithms as the number of …

Presents the introductory welcome message from the conference proceedings.

The volume of existing graph-structured data requires improved parallel tools and algorithms. Finding communities, smaller sub graphs …

The current research focus on “big data” problems highlights the scale and complexity of analytics required and the high rate at which …

Breadth-first search (BFS) is an essential graph traversal strategy widely used in many computing applications. Because of its …

High energy consumption has become a critical problem for supercomputer systems. GPU clusters are becoming an increasingly popular …

Presents the introductory welcome message from the conference proceedings.

Tackling the current volume of graph-structured data requires parallel tools. We extend our work on analyzing such massive graph data …

Current online social networks are massive and still growing. For example, Face book has over 500 million active users sharing over 30 …

A number of graph partitioning algorithms are based on the concept of modularity. In particular Clauset, Newman and Moore (CNM) have …

Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the …

This paper evaluates the performance of the bioinformatics application ClustalW developed on Cell Broadband Engine(TM) (Cell/B.E.) …

Welcome to the 9th International Workshop on High Performance Computational Biology (HiCOMB). Computational Biology and related …

Complex networks capture interactions among entities in various application areas in a graph representation. Analyzing large scale …

Social networks produce an enormous quantity of data. Facebook consists of over 400 million active users sharing over 5 billion pieces …

We present a new approach for parallel massive graph analysis of streaming, temporal data with a dynamic and extensible representation. …

Welcome to Atlanta, Georgia and to the 24th International Parallel and Distributed Processing Symposium. It has been my honor to spend …

The accuracy of Conditional Random Fields (CRF) is achieved at the cost of huge amount of computation to train model. In this paper we …

Many important problems in computational sciences, social network analysis, security, and business analytics, are data-intensive and …

We present a new lock-free parallel algorithm for computing betweenness centrality of massive complex networks that achieves better …

To explore chip-level parallelism, the PSC (Parallel Shared Cache) model is provided in this paper to describe high performance shared …

Due to power wall, memory wall, and ILP wall, we are facing the end of ever increasing single-threaded performance. For this reason, …

Graph-theoretic abstractions are extensively used to analyze massive data sets. Temporal data streams from socio-economic interactions, …

We present a new parallel algorithm that extends and generalizes the traditional graph analysis metric of betweenness centrality to …

The prediction of the correct secondary structures of large RNAs is one of the unsolved challenges of computational molecular biology. …

Current mathematical modeling methods for the spreading of infectious diseases are too simplified and do not scale well. We present the …

In this paper, we empirically evaluate fundamental design trade-offs among the most recent multicore processors and accelerator …

The large L2 cache’s access latency, which is mainly caused by wire delay, is a critical problem to improve the performance of …

In this paper we briefly introduce our new framework, called “design optimizer for scientific applications” (DOSA) which …

High performance computing is critical for financial markets where analysts seek to accelerate complex optimizations such as pricing …

The Sony-Toshiba-IBM Cell Broadband Engine is a heterogeneous multicore architecture that consists of a traditional microprocessor …

Numerical simulations in computational physics, biology, and finance, often require the use of high quality and efficient parallel …

JPEG2000 is the latest still image coding standard from the JPEG committee, which adopts new algorithms such as embedded block coding …

Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as …

We present SNAP (small-world network analysis and partitioning), an open-source graph framework for exploratory study and partitioning …

Over the past several years there have been repeated analyses of the potential value of petascale bioinformatics and computational …

Protein-interaction network (PIN) analysis provides valuable insight into an organism’s functional organization and evolutionary …

We present a study of multithreaded implementations of Thorup’s algorithm for solving the single source shortest path (SSSP) …

We present an experimental study of the single source shortest path problem with non-negative edge weights (NSSP) on large-scale graphs …

Betweenness is a centrality measure based on shortest paths, widely used in complex network analysis. It is computationally-expensive …

In this work, we propose an application composition system (ACS) that allows design-time exploration and automatic run-time …

The Fast Fourier Transform (FFT) is of primary importance and a fundamental kernel in many computationally intensive scientific …

Graph-theoretic abstractions are extensively used to analyze massive data sets. Temporal data streams from socio-economic interactions, …

The Sony-Toshiba-IBM Cell Broadband Engine is a heterogeneous multicore architecture that consists of a traditional microprocessor …

Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as …

Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as …

Due to fundamental physical limitations and power constraints, we are witnessing a radical change in commodity microprocessor …

Graph problems are finding increasing applications in high performance computing disciplines. Although many regular problems can be …

Graph abstractions are extensively used to understand and solve challenging computational problems in various scientific and …

Constructing phylogenetic trees in the study of the evolutionary history of a group organisms is an extremely challenging problem in …

The high computational requirements of several applications in computational genomics are aggravated by an exponential growth in …

This paper discusses fast parallel algorithms for evaluating several centrality indices frequently used in complex network analysis. …

We present an experimental study of parallel algorithms for solving the single source shortest path problem with non-negative edge …

The ability to understand the factors contributing to parallel program performance are vital for understanding the impact of machine …

The maximum flow problem is a combinatorial problem of significant importance in a wide variety of research and commercial …

We compare parallel algorithms for random permutation generation on symmetric multiprocessors (SMPs). Algorithms considered are the …

We present an experimental study of parallel biconnected components algorithms employing several fundamental parallel primitives, e.g., …

The exponential growth in the amount of genomic data has spurred growing interest in large scale analysis of genetic information. …

Graph theoretic problems are representative of fundamental computations in traditional and emerging scientific disciplines like …

Many large-scale optimization problems rely on graph theoretic solutions; yet high-performance computing has traditionally focused on …

Advances in experimental techniques have transformed biology into a data-intensive science, with a rapid explosion of data at the …

Combinatorial problems such as those from graph theory pose serious challenges for parallel machines due to non-contiguous, concurrent …

We focus on implementing parallel spanning tree algorithms on SMPs. Spanning tree is an important problem in the sense that it is the …

We have developed a high performance hybridized parallel finite difference time domain (FDTD) algorithm featuring both OpenMP shared …

This paper summarizes the design and implementation of a parallel algorithm for state assignment of large Finite State Machines (FSMs). …

Minimum spanning tree (MST) is one of the most studied combinatorial problems with practical applications in VLSI layout, wireless …

Bioinformatics is the science of managing, mining, and interpreting information from biological sequences and structures. Genome …

Lock-free shared data structures in the setting of distributed computing have received a fair amount of attention. Major motivations of …

Many parallel algorithms for graph problems start with finding a spanning tree and rooting the tree to define some structural …

The ability to provide uniform shared-memory access to a significant number of processors in a single SMP node brings us much closer to …

The rapid accumulation of whole genome sequences for a wide diversity of taxa is generating a huge amount of comparative data for …

Hannenhalli and Pevzner gave the first polynomial-time algorithm for computing the inversion distance between two signed permutations, …

Phylogenies derived from gene order data may prove crucial in answering some fundamental open questions in biomolecular evolution. Yet …

Phylogeny reconstruction from molecular data poses complex optimization problems: almost all optimization models are NP-hard and thus …

Phylogenies (that is, tree-of-life relationships) derived from gene order data may prove crucial in answering some fundamental open …

The ability to provide uniform shared-memory access to a significant number of processors in a single SMP node brings us much closer to …

The future of high-performance computing relies on the efficient and scalable use of clusters with symmetric multiprocessor (SMP) nodes …

A novel indexing scheme is described to catalogue satellite data on a pixel basis. The objective of this research is to develop an …

This paper discusses high performance clustering from a series of critical topics: architectural design, system software …

Presents efficient and portable implementations of a useful image enhancement process, the symmetric neighborhood filter (SNF), and an …

A fundamental challenge for parallel computing is to obtain high-level, architecture independent, algorithms which execute efficiently …

A common statistical problem is that of finding the median element in a set of data. This paper presents a fast and portable parallel …

This paper presents efficient and portable implementations of two useful primitives in image processing algorithms, histogramming and …