Special Issue "Algorithms for Complex Network Analysis"

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

Deadline for manuscript submissions: closed (31 March 2016).

Special Issue Editor

Guest Editor
Prof. Dr. Natarajan Meghanathan

Department of Computer Science, Jackson State University, Jackson, MS 39217, USA
Website | E-Mail
Phone: +1-601-979-3661
Fax: +1-601-979-2478
Interests: algorithms for complex network analysis; graph theory algorithms, wireless ad hoc networks and sensor networks, machine learning algorithms

Special Issue Information

Dear Colleagues,

With the phenomenal growth of various state-of-the-art networking domains (social networks, wireless networks, web, etc.), and the enriched information available on classical networks (biological networks, citation networks, etc.), our need for a comprehensive suite of algorithms to analyze all of these networks, from both a theoretical and practical standpoint, has become imperative. Through this Special Issue, we invite authors to contribute papers that address the need for developing algorithms for complex network analysis, visualization of large complex network graphs, and the development of efficient methods to study the characteristics of networks involving hundreds or thousands of nodes. Some of the topics of interest include (but are not limited to): community detection algorithms, algorithms for information cascades and diffusion, algorithms for web analytics, algorithms for random graph models, algorithms for scale-free and small-world networks, analysis of biological networks, algorithms for novel centrality measures, etc. We also seek papers that compare the algorithms behind the implementation of various network analysis tools, as well as propose extensions to these algorithms, for analyzing characteristics that are yet to be explored in the literature. Additionally, we invite papers that would apply the algorithms for complex network analysis to various real-world networks (such as the Internet) and Wireless network domains (such as ad hoc networks and sensor networks). We seek high quality papers that clearly delineate its unique contributions to the literature and highlight its differences from that of related work.

Prof. Dr. Natarajan Meghanathan
Guest Editor

Manuscript Submission Information

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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 1000 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

  • Social Network Analysis
  • Centrality Measures
  • Web Analytics
  • Graph Theory Algorithms
  • Community Detection Algorithms

Published Papers (4 papers)

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Research

Open AccessArticle
MultiAspect Graphs: Algebraic Representation and Algorithms
Algorithms 2017, 10(1), 1; https://doi.org/10.3390/a10010001
Received: 25 September 2016 / Revised: 12 December 2016 / Accepted: 19 December 2016 / Published: 25 December 2016
Cited by 5 | PDF Full-text (778 KB) | HTML Full-text | XML Full-text
Abstract
We present the algebraic representation and basic algorithms for MultiAspect Graphs (MAGs). A MAG is a structure capable of representing multilayer and time-varying networks, as well as higher-order networks, while also having the property of being isomorphic to a directed graph. In particular, [...] Read more.
We present the algebraic representation and basic algorithms for MultiAspect Graphs (MAGs). A MAG is a structure capable of representing multilayer and time-varying networks, as well as higher-order networks, while also having the property of being isomorphic to a directed graph. In particular, we show that, as a consequence of the properties associated with the MAG structure, a MAG can be represented in matrix form. Moreover, we also show that any possible MAG function (algorithm) can be obtained from this matrix-based representation. This is an important theoretical result since it paves the way for adapting well-known graph algorithms for application in MAGs. We present a set of basic MAG algorithms, constructed from well-known graph algorithms, such as degree computing, Breadth First Search (BFS), and Depth First Search (DFS). These algorithms adapted to the MAG context can be used as primitives for building other more sophisticated MAG algorithms. Therefore, such examples can be seen as guidelines on how to properly derive MAG algorithms from basic algorithms on directed graphs. We also make available Python implementations of all the algorithms presented in this paper. Full article
(This article belongs to the Special Issue Algorithms for Complex Network Analysis)
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Open AccessArticle
Utilizing Network Structure to Accelerate Markov Chain Monte Carlo Algorithms
Algorithms 2016, 9(3), 50; https://doi.org/10.3390/a9030050
Received: 28 April 2016 / Revised: 8 July 2016 / Accepted: 22 July 2016 / Published: 29 July 2016
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Abstract
We consider the problem of estimating the measure of subsets in very large networks. A prime tool for this purpose is the Markov Chain Monte Carlo (MCMC) algorithm. This algorithm, while extremely useful in many cases, still often suffers from the drawback of [...] Read more.
We consider the problem of estimating the measure of subsets in very large networks. A prime tool for this purpose is the Markov Chain Monte Carlo (MCMC) algorithm. This algorithm, while extremely useful in many cases, still often suffers from the drawback of very slow convergence. We show that in a special, but important case, it is possible to obtain significantly better bounds on the convergence rate. This special case is when the huge state space can be aggregated into a smaller number of clusters, in which the states behave approximately the same way (but their behavior still may not be identical). A Markov chain with this structure is called quasi-lumpable. This property allows the aggregation of states (nodes) into clusters. Our main contribution is a rigorously proved bound on the rate at which the aggregated state distribution approaches its limit in quasi-lumpable Markov chains. We also demonstrate numerically that in certain cases this can indeed lead to a significantly accelerated way of estimating the measure of subsets. The result can be a useful tool in the analysis of complex networks, whenever they have a clustering that aggregates nodes with similar (but not necessarily identical) behavior. Full article
(This article belongs to the Special Issue Algorithms for Complex Network Analysis)
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Open AccessArticle
Data Filtering Based Recursive and Iterative Least Squares Algorithms for Parameter Estimation of Multi-Input Output Systems
Algorithms 2016, 9(3), 49; https://doi.org/10.3390/a9030049
Received: 24 April 2016 / Revised: 19 July 2016 / Accepted: 20 July 2016 / Published: 26 July 2016
Cited by 6 | PDF Full-text (264 KB) | HTML Full-text | XML Full-text
Abstract
This paper discusses the parameter estimation problems of multi-input output-error autoregressive (OEAR) systems. By combining the auxiliary model identification idea and the data filtering technique, a data filtering based recursive generalized least squares (F-RGLS) identification algorithm and a data filtering based iterative least [...] Read more.
This paper discusses the parameter estimation problems of multi-input output-error autoregressive (OEAR) systems. By combining the auxiliary model identification idea and the data filtering technique, a data filtering based recursive generalized least squares (F-RGLS) identification algorithm and a data filtering based iterative least squares (F-LSI) identification algorithm are derived. Compared with the F-RGLS algorithm, the proposed F-LSI algorithm is more effective and can generate more accurate parameter estimates. The simulation results confirm this conclusion. Full article
(This article belongs to the Special Issue Algorithms for Complex Network Analysis)
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Graphical abstract

Open AccessArticle
A Greedy Algorithm for Neighborhood Overlap-Based Community Detection
Algorithms 2016, 9(1), 8; https://doi.org/10.3390/a9010008
Received: 24 October 2015 / Revised: 1 January 2016 / Accepted: 6 January 2016 / Published: 11 January 2016
Cited by 9 | PDF Full-text (3644 KB) | HTML Full-text | XML Full-text
Abstract
The neighborhood overlap (NOVER) of an edge u-v is defined as the ratio of the number of nodes who are neighbors for both u and v to that of the number of nodes who are neighbors of at least u or [...] Read more.
The neighborhood overlap (NOVER) of an edge u-v is defined as the ratio of the number of nodes who are neighbors for both u and v to that of the number of nodes who are neighbors of at least u or v. In this paper, we hypothesize that an edge u-v with a lower NOVER score bridges two or more sets of vertices, with very few edges (other than u-v) connecting vertices from one set to another set. Accordingly, we propose a greedy algorithm of iteratively removing the edges of a network in the increasing order of their neighborhood overlap and calculating the modularity score of the resulting network component(s) after the removal of each edge. The network component(s) that have the largest cumulative modularity score are identified as the different communities of the network. We evaluate the performance of the proposed NOVER-based community detection algorithm on nine real-world network graphs and compare the performance against the multi-level aggregation-based Louvain algorithm, as well as the original and time-efficient versions of the edge betweenness-based Girvan-Newman (GN) community detection algorithm. Full article
(This article belongs to the Special Issue Algorithms for Complex Network Analysis)
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