
Oliver Alvarado RodriguezNew Jersey Institute of Technology | NJIT · Department of Computer Science
Oliver Alvarado Rodriguez
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
January 2018 - May 2020
Education
September 2020 - May 2026
September 2016 - May 2020
Publications
Publications (14)
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...
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...
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...
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...
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...
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...