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Massimo Franceschet

Publications and source records attributed to Massimo Franceschet.

At least 19 recordsLinked to original sources
arXiv2021

HITS hits art

The blockchain art market is partitioned around the roles of artists and collectors and highly concentrated among few prominent figures. We hence propose to adapt Kleinberg's authority/hub HITS method to rate artists and collectors in the art context. This seems a reasonable choice since the original method deftly defines its scores in terms of a mutual recursive relationship between authorities/artists - the miners of information/art, and hubs/collectors - the assemblers of such information/art. We evaluated the proposed method on the collector-artist network of SuperRare gallery, the major crypto art marketplace. We found that the proposed artist and collector metrics are weakly correlated with other network science metrics like degree and strength. This hints the possibility of coupling different measures in order to profile active users of the gallery and suggests investment strategies with different risk/reward ratios for collectors as well as marketing strategies with different targets for artists.

cs.CY↗
arXiv2020

Quantifying the higher-order influence of scientific publications

Citation impact is commonly assessed using direct, first-order citation relations. We consider here instead the indirect influence of publications on new publications via citations. We present a novel method to quantify the higher-order citation influence of publications, considering both direct, or first-order, and indirect, or higher-order citations. In particular, we are interested in higher-order citation influence at the level of disciplines. We apply this method to the whole Web of Science data at the level of disciplines. We find that a significant amount of influence -- 42% -- stems from higher-order citations. Furthermore, we show that higher-order citation influence is helpful to quantify and visualize citation flows among disciplines, and to assess their degree of interdisciplinarity.

cs.DL↗
arXiv2019

Crypto art: A decentralized view

This is a decentralized position paper on crypto art, which includes viewpoints from different actors of the system: artists, collectors, galleries, art scholars, data scientists. The writing process went as follows: a general definition of the topic was put forward by two of the authors (Franceschet and Colavizza), and used as reference to ask to a set of diverse authors to contribute with their viewpoints asynchronously and independently. No guidelines were offered before the first draft, if not to reach a minimum of words to justify a separate section/contribution. Afterwards, all authors read and commented on each other's work and minimal editing was done. Every author was asked to suggest open questions and future perspectives on the topic of crypto art from their vantage point, while keeping full control of their own sections at all times. While this process does not necessarily guarantee the uniformity expected from, say, a research article, it allows for multiple voices to emerge and provide for a contribution on a common topic. The ending section offers an attempt to pull all these threads together into a perspective on the future of crypto art.

cs.CY↗
arXiv2017

Arbitrarily regularizable graphs

A graph is regularizable if it is possible to assign weights to its edges so that all nodes have the same degree. Weights can be positive, nonnegative or arbitrary as soon as the regularization degree is not null. Positive and nonnegative regularizable graphs have been thoroughly investigated in the literature. In this work, we propose and study arbitrarily regularizable graphs. In particular, we investigate necessary and sufficient regularization conditions on the topology of the graph and of the corresponding adjacency matrix. Moreover, we study the computational complexity of the regularization problem and characterize it as a linear programming model.

cs.SI↗
arXiv2017

The temporalized Massey's method

We propose and throughly investigate a temporalized version of the popular Massey's technique for rating actors in sport competitions. The method can be described as a dynamic temporal process in which team ratings are updated at every match according to their performance during the match and the strength of the opponent team. Using the Italian soccer dataset, we empirically show that the method has a good foresight prediction accuracy.

cs.SI↗
arXiv2016

A theory on power in networks

The eigenvector centrality equation $λx = A \, x$ is a successful compromise between simplicity and expressivity. It claims that central actors are those connected with central others. For at least 70 years, this equation has been explored in disparate contexts, including econometrics, sociometry, bibliometrics, Web information retrieval, and network science. We propose an equally elegant counterpart: the power equation $x = A x^÷$, where $x^÷$ is the vector whose entries are the reciprocal of those of $x$. It asserts that power is in the hands of those connected with powerless others. It is meaningful, for instance, in bargaining situations, where it is advantageous to be connected to those who have few options. We tell the parallel, mostly unexplored story of this intriguing equation.

cs.SI↗
arXiv2014

Vulnerability and power on networks

Inspired by socio-political scenarios, like dictatorships, in which a minority of people exercise control over a majority of weakly interconnected individuals, we propose vulnerability and power measures defined on groups of actors of networks. We establish an unexpected connection between network vulnerability and graph regularizability. We use the Shapley value of coalition games to introduce fresh notions of vulnerability and power at node level defined in terms of the corresponding measures at group level. We investigate the computational complexity of computing the defined measures, both at group and node levels, and provide effective methods to quantify them. Finally we test vulnerability and power on both artificial and real networks.

cs.SI↗
arXiv2014

Complex Beauty

Complex systems and their underlying convoluted networks are ubiquitous, all we need is an eye for them. They pose problems of organized complexity which cannot be approached with a reductionist method. Complexity science and its emergent sister network science both come to grips with the inherent complexity of complex systems with an holistic strategy. The relevance of complexity, however, transcends the sciences. Complex systems and networks are the focal point of a philosophical, cultural and artistic turn of our tightly interrelated and interdependent postmodern society. Here I take a different, aesthetic perspective on complexity. I argue that complex systems can be beautiful and can the object of artification - the neologism refers to processes in which something that is not regarded as art in the traditional sense of the word is changed into art. Complex systems and networks are powerful sources of inspiration for the generative designer, for the artful data visualizer, as well as for the traditional artist. I finally discuss the benefits of a cross-fertilization between science and art.

cs.OH↗
arXiv2013

Computing the smallest eigenpairs of the graph Laplacian

The graph Laplacian, a typical representation of a network, is an important matrix that can tell us much about the network structure. In particular its eigenpairs (eigenvalues and eigenvectors) incubate precious topological information about the network at hand, including connectivity, partitioning, node distance and centrality. Real networks might be very large in number of nodes (actors); luckily, most real networks are sparse, meaning that the number of edges (binary connections among actors) are few with respect to the maximum number of possible edges. In this paper we experimentally compare three state-of-the-art algorithms for computation of a few among the smallest eigenpairs of large and sparse matrices: the Implicitly Restarted Lanczos Method, which is the current implementation in the most popular scientific computing environments (Matlab \R), the Jacobi-Davidson method, and the Deflation Accelerated Conjugate Gradient method. We implemented the algorithms in a uniform programming setting and tested them over diverse real-world networks including biological, technological, information, and social networks. It turns out that the Jacobi-Davidson method displays the best performance in terms of number of matrix-vector products and CPU time.

math.NA↗
arXiv2012

Effective and efficient approximations of the generalized inverse of the graph Laplacian matrix with an application to current-flow betweenness centrality

We devise methods for finding approximations of the generalized inverse of the graph Laplacian matrix, which arises in many graph-theoretic applications. Finding this matrix in its entirety involves solving a matrix inversion problem, which is resource demanding in terms of consumed time and memory and hence impractical whenever the graph is relatively large. Our approximations use only few eigenpairs of the Laplacian matrix and are parametric with respect to this number, so that the user can compromise between effectiveness and efficiency of the approximated solution. We apply the devised approximations to the problem of computing current-flow betweenness centrality on a graph. However, given the generality of the Laplacian matrix, many other applications can be sought. We experimentally demonstrate that the approximations are effective already with a constant number of eigenpairs. These few eigenpairs can be stored with a linear amount of memory in the number of nodes of the graph and, in the realistic case of sparse networks, they can be efficiently computed using one of the many methods for retrieving few eigenpairs of sparse matrices that abound in the literature.

cs.SI↗
arXiv2011

The large-scale structure of journal citation networks

We analyse the large-scale structure of the journal citation network built from information contained in the Thomson-Reuters Journal Citation Reports. To this end, we take advantage of the network science paraphernalia and explore network properties like density, percolation robustness, average and largest node distances, reciprocity, incoming and outgoing degree distributions, as well as assortative mixing by node degrees. We discover that the journal citation network is a dense, robust, small, and reciprocal world. Furthermore, in and out node degree distributions display long-tails, with few vital journals and many trivial ones, and they are strongly positively correlated.

cs.SI↗
arXiv2011

Collaboration in computer science: a network science approach. Part I

Co-authorship in publications within a discipline uncovers interesting properties of the analysed field. We represent collaboration in academic papers of computer science in terms of differently grained networks, including those sub-networks that emerge from conference and journal co-authorship only. We take advantage of the network science paraphernalia to take a picture of computer science collaboration including all papers published in the field since 1936. We investigate typical bibliometric properties like scientific productivity of authors and collaboration level in papers, as well as large-scale network properties like reachability and average separation distance among scholars, distribution of the number of scholar collaborators, network resilience and dependence on star collaborators, network clustering, and network assortativity by number of collaborators.

cs.SI↗
arXiv2011

Collaboration in computer science: a network science approach. Part II

We represent collaboration of authors in computer science papers in terms of both affiliation and collaboration networks and observe how these networks evolved over time since 1960. We investigate the temporal evolution of bibliometric properties, like size of the discipline, productivity of scholars, and collaboration level in papers, as well as of large-scale network properties, like reachability and average separation distance among scientists, distribution of the number of scholar collaborators, network clustering and network assortativity by number of collaborators.

cs.SI↗
arXiv2010

PageRank: Standing on the shoulders of giants

PageRank is a Web page ranking technique that has been a fundamental ingredient in the development and success of the Google search engine. The method is still one of the many signals that Google uses to determine which pages are most important. The main idea behind PageRank is to determine the importance of a Web page in terms of the importance assigned to the pages hyperlinking to it. In fact, this thesis is not new, and has been previously successfully exploited in different contexts. We review the PageRank method and link it to some renowned previous techniques that we have found in the fields of Web information retrieval, bibliometrics, sociometry, and econometrics.

cs.IR↗
arXiv2010

A logic for networks

Networks are pervasive in the real world. Nature, society, economy, and technology are supported by ostensibly different networks that in fact share an amazing number of interesting structural properties. Network thinking exploded in the last decade, boosted by the availability of large databases on the topology of various real networks, mainly the Web and biological networks, and converged to the new discipline of network analysis - the holistic analysis of complex systems through the study of the network that wires their components. Physicists mainly drove the investigation, studying the structure and function of networks using methods and tools of statistical mechanics. Here, we give an alternative perspective on network analysis, proposing a logic for specifying general properties of networks and a modular algorithm for checking these properties. The logic borrows from two intertwined computing fields: XML databases and model checking.

cs.LO↗
arXiv2010

The skewness of computer science

Computer science is a relatively young discipline combining science, engineering, and mathematics. The main flavors of computer science research involve the theoretical development of conceptual models for the different aspects of computing and the more applicative building of software artifacts and assessment of their properties. In the computer science publication culture, conferences are an important vehicle to quickly move ideas, and journals often publish deeper versions of papers already presented at conferences. These peculiarities of the discipline make computer science an original research field within the sciences, and, therefore, the assessment of classical bibliometric laws is particularly important for this field. In this paper, we study the skewness of the distribution of citations to papers published in computer science publication venues (journals and conferences). We find that the skewness in the distribution of mean citedness of different venues combines with the asymmetry in citedness of articles in each venue, resulting in a highly asymmetric citation distribution with a power law tail. Furthermore, the skewness of conference publications is more pronounced than the asymmetry of journal papers. Finally, the impact of journal papers, as measured with bibliometric indicators, largely dominates that of proceeding papers.

cs.DL↗
arXiv2009

The first Italian research assessment exercise: a bibliometric perspective

In December 2003, seventeen years after the first UK research assessment exercise, Italy started up its first-ever national research evaluation, with the aim to evaluate, using the peer review method, the excellence of the national research production. The evaluation involved 20 disciplinary areas, 102 research structures, 18,500 research products and 6,661 peer reviewers (1,465 from abroad); it had a direct cost of 3.55 millions Euros and a time length spanning over 18 months. The introduction of ratings based on ex post quality of output and not on ex ante respect for parameters and compliance is an important leap forward of the national research evaluation system toward meritocracy. From the bibliometric perspective, the national assessment offered the unprecedented opportunity to perform a large-scale comparison of peer review and bibliometric indicators for an important share of the Italian research production. The present investigation takes full advantage of this opportunity to test whether peer review judgements and (article and journal) bibliometric indicators are independent variables and, in the negative case, to measure the sign and strength of the association. Outcomes allow us to advocate the use of bibliometric evaluation, suitably integrated with expert review, for the forthcoming national assessment exercises, with the goal of shifting from the assessment of research excellence to the evaluation of average research performance without significant increase of expenses.

cs.CY↗