Hybrid journal (It can contain Open Access articles) ISSN (Print) 2051-1310 - ISSN (Online) 2051-1329 Published by Oxford University Press[396 journals]

Authors:Snellman J; Iñiguez G, Govezensky T, et al. Pages: 817 - 838 Abstract: In human societies, people’s willingness to compete and strive for better social status, as well as being envious of those perceived in some way superior, lead to social structures that are intrinsically hierarchical. Here, we propose an agent-based, network model to mimic the ranking behaviour of individuals and its possible repercussions in human society. The main ingredient of the model is the assumption that the relevant feature of social interactions is each individual’s keenness to maximize his or her status relative to others. The social networks produced by the model are homophilous and assortative, as frequently observed in human communities, and most of the network properties seem quite independent of its size. However, we see that for a small number of agents the resulting network consists of disjoint weakly connected communities, while being highly assortative and homophilic. On the other hand, larger networks turn out to be more cohesive with larger communities but less homophilic. We find that the reason for these changes is that larger network size allows agents to use new strategies for maximizing their social status, allowing for more diverse links between them. PubDate: Fri, 16 Jun 2017 00:00:00 GMT DOI: 10.1093/comnet/cnx009 Issue No:Vol. 5, No. 6 (2017)

Authors:Boroojeni A; Dewar J, Wu T, et al. Pages: 839 - 857 Abstract: We describe a class of new algorithms to construct bipartite networks that preserves a prescribed degree and joint-degree (degree–degree) distribution of the nodes. Bipartite networks are graphs that can represent real-world interactions between two disjoint sets, such as actor–movie networks, author–article networks, co-occurrence networks and heterosexual partnership networks. Often there is a strong correlation between the degree of a node and the degrees of the neighbours of that node that must be preserved when generating a network that reflects the structure of the underling system. Our bipartite $2K$ ($B2K$) algorithms generate an ensemble of networks that preserve prescribed degree sequences for the two disjoint set of nodes in the bipartite network, and the joint-degree distribution that is the distribution of the degrees of all neighbours of nodes with the same degree. We illustrate the effectiveness of the algorithms on a romance network using the NetworkX software environment to compare other properties of a target network that are not directly enforced by the $B2K$ algorithms. We observe that when average degree of nodes is low, as is the case for romance and heterosexual partnership networks, then the $B2K$ networks tend to preserve additional properties, such as the cluster coefficients, than algorithms that do not preserve the joint-degree distribution of the original network. PubDate: Tue, 27 Jun 2017 00:00:00 GMT DOI: 10.1093/comnet/cnx014 Issue No:Vol. 5, No. 6 (2017)

Authors:Wang J; Wilson R, Hancock E. Pages: 858 - 883 Abstract: This article explores the thermodynamic characterization of networks using the heat bath analogy when the energy states are occupied under different spin statistics, specified by a partition function. Using the heat bath analogy and a matrix characterization for the Hamiltonian operator, we consider the cases where the energy states are occupied according to Maxwell–Boltzmann, Bose–Einstein and Fermi–Dirac statistics. We derive expressions for thermodynamic variables, such as entropy, for the system with particles occupying the energy states given by the normalized Laplacian eigenvalues. The chemical potential determines the number of particles at a given temperature. We provide the systematic study of the entropic measurements for network complexity resulting from the different partition functions and specifically those associated with alternative assumptions concerning the spin statistics. Compared with the network von Neumann entropy corresponding to the normalized Laplacian matrix, these entropies are effective in characterizing the significant structural configurations and distinguishing different types of network models (Erdős–Rényi random graphs, Watts–Strogatz small world networks and Barabási–Albert scale-free networks). The effect of the spin statistics is (a) in the case of bosons to allow the particles in the heat bath to congregate in the lower energy levels and (b) in the case of fermions to populate higher energy levels. With normalized Laplacian energy states, this means that bosons are more sensitive to the spectral gap and hence to cluster or community structure, and fermions better sample the distribution of path lengths in a network. Numerical experiments for synthetic and real-world data sets are presented to evaluate the qualitative and quantitative differences of the thermodynamic network characterizations derived from the different occupation statistics, and these confirm these qualitative intuitions. PubDate: Wed, 05 Jul 2017 00:00:00 GMT DOI: 10.1093/comnet/cnx017 Issue No:Vol. 5, No. 6 (2017)

Authors:Scoville N; Yegnesh K. Pages: 884 - 892 Abstract: Persistent homology has recently emerged as a powerful technique in topological data analysis for analysing the emergence and disappearance of topological features throughout a filtered space, shown via persistence diagrams. In this article, we develop an application of ideas from the theory of persistent homology and persistence diagrams to the study of data flow malfunctions in networks with a certain hierarchical structure. In particular, we formulate an algorithmic construction of persistence diagrams that parameterize network data flow errors, thus enabling novel applications of statistical methods that are traditionally used to assess the stability of persistence diagrams corresponding to homological data to the study of data flow malfunctions. We conclude with an application to network packet delivery systems. PubDate: Thu, 24 Aug 2017 00:00:00 GMT DOI: 10.1093/comnet/cnx038 Issue No:Vol. 5, No. 6 (2017)

Authors:Rossetti G. Pages: 893 - 912 Abstract: Graph models provide an understanding of the dynamics of network formation and evolution; as a direct consequence, synthesizing graphs having controlled topology and planted partitions has been often identified as a strategy to describe benchmarks able to assess the performances of community discovery algorithm. However, one relevant aspect of real-world networks has been ignored by benchmarks proposed so far: community dynamics. As time goes by network communities rise, fall and may interact with each other generating merges and splits. Indeed, during the last decade dynamic community discovery has become a very active research field: in order to provide a coherent environment to test novel algorithms aimed at identifying mutable network partitions we introduce $\text{RD}\small{\text{YN}}$, an approach able to generates dynamic networks along with time-dependent ground-truth partitions having tunable quality. PubDate: Wed, 05 Jul 2017 00:00:00 GMT DOI: 10.1093/comnet/cnx016 Issue No:Vol. 5, No. 6 (2017)

Authors:da Cunha B; Gonçalves S. Pages: 913 - 923 Abstract: Vulnerabilities of complex networks have become a trend topic in complex systems due to its applications to real-world problems. Most real networks tend to be very fragile to sequential attacks such as high-degree adaptive or collective influence. However, recent contributions have shown the importance of interconnected nodes in the integrity of networks and module-based attacks have appeared very promising when compared to traditional malicious non-adaptive attacks. In this article, we study in detail the trade-off between robustness and running time of modern dismantling algorithms in modular networks. To do so, we introduce a generalized robustness measure and an empirical quantity aimed to guide the best choice of attack strategy in real cases, which we call the attack’s performance. We show that the computational complexity of the module-based attack runs linearly as long as $N_{d}<\log{N}$, where $N_{d}$ is the number of bridges among communities and $N$ is the size of the network. Taken into account both the generalized robustness and the computational complexity/running time we show that the non-adaptive module-based method performs better than other adaptive attacks in networks with well-defined community structures. PubDate: Tue, 27 Jun 2017 00:00:00 GMT DOI: 10.1093/comnet/cnx015 Issue No:Vol. 5, No. 6 (2017)

Authors:Zino L; Rizzo A, Porfiri M. Pages: 924 - 952 Abstract: Network theory has greatly contributed to an improved understanding of epidemic processes, offering an empowering framework for the analysis of real-world data, prediction of disease outbreaks, and formulation of containment strategies. However, the current state of knowledge largely relies on time-invariant networks, which are not adequate to capture several key features of a number of infectious diseases. Activity driven networks (ADNs) constitute a promising modelling framework to describe epidemic spreading over time varying networks, but a number of technical and theoretical gaps remain open. Here, we lay the foundations for a novel theory to model general epidemic spreading processes over time-varying, ADNs. Our theory derives a continuous-time model, based on ordinary differential equations (ODEs), which can reproduce the dynamics of any discrete-time epidemic model evolving over an ADN. A rigorous, formal framework is developed, so that a general epidemic process can be systematically mapped, at first, on a Markov jump process, and then, in the thermodynamic limit, on a system of ODEs. The obtained ODEs can be integrated to simulate the system dynamics, instead of using computationally intensive Monte Carlo simulations. An array of mathematical tools for the analysis of the proposed model is offered, together with techniques to approximate and predict the dynamics of the epidemic spreading, from its inception to the endemic equilibrium. The theoretical framework is illustrated step-by-step through the analysis of a susceptible–infected–susceptible process. Once the framework is established, applications to more complex epidemic models are presented, along with numerical results that corroborate the validity of our approach. Our framework is expected to find application in the study of a number of critical phenomena, including behavioural changes due to the infection, unconscious spread of the disease by exposed individuals, or the removal of nodes from the network of contacts. PubDate: Mon, 13 Nov 2017 00:00:00 GMT DOI: 10.1093/comnet/cnx056 Issue No:Vol. 5, No. 6 (2017)

Authors:Tennant A; Ahmad N, Derrible S. Pages: 953 - 963 Abstract: A general model of the complexity of the sport of boxing has yet to be produced exploring the match play that goes on between combatants. The sport has a long history that dates back to the eighth century before common era (BCE) to the time of ancient Greece. Also known as the ‘sweet science’, most research work has legitimately focused on the combat sport’s long-term health affects concerning brain trauma. This present study seeks to explore the complexity of the sport by utilizing a data set of welterweights (63.5–67 kg). This data set was used to build a contact network with the boxers as nodes and the actual fights as the links. Additionally a PageRank algorithm was used to rank the boxers from the contact network. Devon Alexander was calculated as the top welterweight from data set. This was compared with the rankings of the sport’s notoriously corrupt sanctioning bodies, journalistic rankings, and a more standard non-network based ranking system. The network visualization displayed features typical of many others seen in the literature. A closer look was taken on several of the boxers by the visualization technique known as the rank clock. This allowed for the boxer’s rank history to be tracked and allowed for insight on their career trajectory. Timothy Bradley and Vyacheslav Senchenko had rank clocks that displayed them to be the most consistent boxers in the 2004–2014 decade. These research findings supply further confirmation of value of the network based approach in athletic match play. PubDate: Thu, 29 Jun 2017 00:00:00 GMT DOI: 10.1093/comnet/cnx010 Issue No:Vol. 5, No. 6 (2017)

Authors:Lee H; Malik N, Mucha P. Pages: 964 - 964 Abstract: The funding section of this article should read: PubDate: Tue, 18 Jul 2017 00:00:00 GMT DOI: 10.1093/comnet/cnx030 Issue No:Vol. 5, No. 6 (2017)