Oliver Alvarado Rodriguez

Oliver Alvarado Rodriguez
New Jersey Institute of Technology | NJIT · Department of Computer Science

Bachelor of Science
Computer Science Ph.D. candidate at NJIT.

About

14
Publications
591
Reads
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28
Citations
Introduction
Currently working on the design and implementation of parallel and distributed graph algorithms.
Additional affiliations
May 2021 - present
New Jersey Institute of Technology
Position
  • Research Assistant
January 2018 - May 2020
William Paterson University
Position
  • Research Assistant
Education
September 2020 - May 2026
New Jersey Institute of Technology
Field of study
  • Computer Science
September 2016 - May 2020
William Paterson University
Field of study
  • Computer Science

Publications

Publications (14)
Conference Paper
Full-text available
This paper introduces a novel, parallel, and scalable implementation of the VF2 algorithm for subgraph monomor-phism developed in the high-productivity language Chapel. Efficient graph analysis in large and complex network datasets is crucial across numerous scientific domains. We address this need through our enhanced VF2-PS implementation, widely...
Article
Full-text available
The hypergraph community detection problem seeks to identify groups of related vertices in hypergraph data. We propose an information-theoretic hypergraph community detection algorithm which compresses the observed data in terms of community labels and community-edge intersections. This algorithm can also be viewed as maximum-likelihood inference i...
Preprint
Full-text available
The hypergraph community detection problem seeks to identify groups of related nodes in hypergraph data. We propose an information-theoretic hypergraph community detection algorithm which compresses the observed data in terms of community labels and community-edge intersections. This algorithm can also be viewed as maximum-likelihood inference in a...
Preprint
Full-text available
Counting and finding triangles in graphs is often used in real-world analytics for characterizing the cohesiveness and identifying communities in graphs. In this paper, we present novel sequential and parallel triangle counting algorithms based on identifying horizontal-edges in a breadth-first search (BFS) traversal of the graph. The BFS allows ou...
Article
Full-text available
Data from emerging applications, such as cybersecurity and social networking, can be abstracted as graphs whose edges are updated sequentially in the form of a stream. The challenging problem of interactive graph stream analytics is the quick response of the queries on terabyte and beyond graph stream data from end users. In this paper, a succinct...
Conference Paper
Machine learning (ML) is becoming a powerful tool for a variety of applications where artificial intelligence solutions are required. A ML benchmark is a standard suite to measure, evaluate and compare the performance and efficiency of ML systems. This study analyzes the benchmark results from two famous benchmarks MLMark and MLPerf to provide a ba...

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