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Algorithms
Special Issues
Scalable Graph Algorithms and Applications
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Scalable Graph Algorithms and Applications

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 6587

Special Issue Editors

Dr. Deepak Ajwani

E-Mail Website
Guest Editor
School of Computer Science, University College Dublin, Dublin, Ireland
Interests: scalable algorithms; machine learning; combinatorial optimization; graph algorithms; algorithm engineering
Dr. Sabine Storandt

E-Mail Website
Guest Editor
Department of Computer and Information Science, University of Konstanz, Konstanz 78467, Germany
Interests: algorithm engineering; graph algorithms; discrete optimization
Dr. Darren Strash

E-Mail Website
Guest Editor
Department of Computer Science, Hamilton College, Clinton, NY 13323, USA
Interests: theoretical computer science; combinatorial optimization; algorithm engineering; geometric and graph algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit your latest research in the area of scalable graph algorithms to the Special Issue, “Scalable Graph Algorithms and Applications”. We solicit high-quality papers to address both algorithm design and algorithm engineering issues related to the processing of large graphs. We consider scalability in a broad sense: (i) for near linear-time graph algorithms such as those related to traversal and shortest-paths, scalability refers to algorithms that address issues of cache-efficiency, I/O-efficiency, GPU parallelism, shared memory parallelism, and distributed algorithms; (ii) for polynomial-time graph algorithms such as those related to matching, flow and centrality computation, scalability refers to the exact and approximate algorithms that scale to millions of nodes and edges; (iii) For NP-hard graph problems such as those related to subgraph pattern matching, scalability refers to the design and engineering of heuristics that are capable of dealing with graphs that have thousands of nodes and tens of thousands of edges. We also invite submissions related to applications of scalable graph algorithms in areas such as social network analysis, navigation systems and sustainable transportation, querying and mining knowledge graphs, semantic web, recommendation systems, financial and economic networks, network epidemic models, bioinformatics, and earth sciences applications.

Dr. Deepak Ajwani
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Cache-efficient graph algorithms
  • I/O-efficient graph algorithms
  • Shared-memory multicore graph algorithms
  • Distributed memory graph algorithms
  • Graph algorithms for GPUs
  • Streaming graph algorithms
  • Randomized graph algorithms
  • Approximate graph algorithms
  • Dynamic graph algorithms
  • Algorithms for large-scale network analytics
  • Scalable graph mining
  • Efficient heuristics for NP-hard graph problems
  • Models for real-world graphs
  • Generation of large graph instances
  • Simulations involving large graphs
  • Applications of large graphs in social network analysis, navigation systems and sustainable transportation, querying and mining knowledge graphs, etc.

Published Papers (2 papers)

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Research

25 pages, 940 KiB  
Article
Similar Supergraph Search Based on Graph Edit Distance
by Masataka Yamada and Akihiro Inokuchi
Algorithms 2021, 14(8), 225; https://0-doi-org.brum.beds.ac.uk/10.3390/a14080225 - 27 Jul 2021
Cited by 5 | Viewed by 2892
Abstract
Subgraph and supergraph search methods are promising techniques for the development of new drugs. For example, the chemical structure of favipiravir—an antiviral treatment for influenza—resembles the structure of some components of RNA. Represented as graphs, such compounds are similar to a subgraph of [...] Read more.
Subgraph and supergraph search methods are promising techniques for the development of new drugs. For example, the chemical structure of favipiravir—an antiviral treatment for influenza—resembles the structure of some components of RNA. Represented as graphs, such compounds are similar to a subgraph of favipiravir. However, the existing supergraph search methods can only discover compounds that match exactly. We propose a novel problem, called similar supergraph search, and design an efficient algorithm to solve it. The problem is to identify all graphs in a database that are similar to any subgraph of a query graph, where similarity is defined as edit distance. Our algorithm represents the set of candidate subgraphs by a code tree, which it uses to efficiently compute edit distance. With a distance threshold of zero, our algorithm is equivalent to an existing efficient algorithm for exact supergraph search. Our experiments show that the computation time increased exponentially as the distance threshold increased, but increased sublinearly with the number of graphs in the database. Full article
(This article belongs to the Special Issue Scalable Graph Algorithms and Applications)
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25 pages, 802 KiB  
Article
Interactive Graph Stream Analytics in Arkouda
by Zhihui Du, Oliver Alvarado Rodriguez, Joseph Patchett and David A. Bader
Algorithms 2021, 14(8), 221; https://0-doi-org.brum.beds.ac.uk/10.3390/a14080221 - 21 Jul 2021
Cited by 8 | Viewed by 2760
Abstract
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 [...] Read more.
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 and efficient double index data structure is designed to build the sketch of a graph stream to meet general queries. A single pass stream model, which includes general sketch building, distributed sketch based analysis algorithms and regression based approximation solution generation, is developed, and a typical graph algorithm—triangle counting—is implemented to evaluate the proposed method. Experimental results on power law and normal distribution graph streams show that our method can generate accurate results (mean relative error less than 4%) with a high performance. All our methods and code have been implemented in an open source framework, Arkouda, and are available from our GitHub repository, Bader-Research. This work provides the large and rapidly growing Python community with a powerful way to handle terabyte and beyond graph stream data using their laptops. Full article
(This article belongs to the Special Issue Scalable Graph Algorithms and Applications)
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