82 research outputs found
A new measure for community structures through indirect social connections
Based on an expert systems approach, the issue of community detection can be
conceptualized as a clustering model for networks. Building upon this further,
community structure can be measured through a clustering coefficient, which is
generated from the number of existing triangles around the nodes over the
number of triangles that can be hypothetically constructed. This paper provides
a new definition of the clustering coefficient for weighted networks under a
generalized definition of triangles. Specifically, a novel concept of triangles
is introduced, based on the assumption that, should the aggregate weight of two
arcs be strong enough, a link between the uncommon nodes can be induced. Beyond
the intuitive meaning of such generalized triangles in the social context, we
also explore the usefulness of them for gaining insights into the topological
structure of the underlying network. Empirical experiments on the standard
networks of 500 commercial US airports and on the nervous system of the
Caenorhabditis elegans support the theoretical framework and allow a comparison
between our proposal and the standard definition of clustering coefficient
Structural Bounds on the Dyadic Effect
In this paper we consider the dyadic effect introduced in complex networks
when nodes are distinguished by a binary characteristic. Under these
circumstances two independent parameters, namely dyadicity and heterophilicity,
are able to measure how much the assigned characteristic affects the network
topology. All possible configurations can be represented in a phase diagram
lying in a two-dimensional space that represents the feasible region of the
dyadic effect, which is bound by two upper bounds on dyadicity and
heterophilicity. Using some network's structural arguments, we are able to
improve such upper bounds and introduce two new lower bounds, providing a
reduction of the feasible region of the dyadic effect as well as constraining
dyadicity and heterophilicity within a specific range. Some computational
experiences show the bounds' effectiveness and their usefulness with regards to
different classes of networks
Towards more effective consumer steering via network analysis
Increased data gathering capacity, together with the spread of data analytics
techniques, has prompted an unprecedented concentration of information related
to the individuals' preferences in the hands of a few gatekeepers. In the
present paper, we show how platforms' performances still appear astonishing in
relation to some unexplored data and networks properties, capable to enhance
the platforms' capacity to implement steering practices by means of an
increased ability to estimate individuals' preferences. To this end, we rely on
network science whose analytical tools allow data representations capable of
highlighting relationships between subjects and/or items, extracting a great
amount of information. We therefore propose a measure called Network
Information Patrimony, considering the amount of information available within
the system and we look into how platforms could exploit data stemming from
connected profiles within a network, with a view to obtaining competitive
advantages. Our measure takes into account the quality of the connections among
nodes as the one of a hypothetical user in relation to its neighbourhood,
detecting how users with a good neighbourhood -- hence of a superior
connections set -- obtain better information. We tested our measures on
Amazons' instances, obtaining evidence which confirm the relevance of
information extracted from nodes' neighbourhood in order to steer targeted
users
Clustering networked funded European research activities through rank-size laws
This paper treats a well-established public evaluation problem, which is the analysis of the funded research projects. We specifically deal with the collection of the research actions funded by the European Union over the 7th Framework Programme for Research and Technological Development and Horizon 2020. The reference period is 2007–2020. The study is developed through three methodological steps. First, we consider the networked scientific institutions by stating a link between two organizations when they are partners in the same funded project. In doing so, we build yearly complex networks. We compute four nodal centrality measures with relevant, informative content for each of them. Second, we implement a rank-size procedure on each network and each centrality measure by testing four meaningful classes of parametric curves to fit the ranked data. At the end of such a step, we derive the best fit curve and the calibrated parameters. Third, we perform a clustering procedure based on the best-fit curves of the ranked data for identifying regularities and deviations among years of research and scientific institutions. The joint employment of the three methodological approaches allows a clear view of the research activity in Europe in recent years
An empirical study of the Enterprise Europe Network
This article offers a network perspective on the collaborative effects of technology transfer, providing a research methodology based on the network science paradigm. We argue that such an approach is able to map and describe the set of entities acting in the technology transfer environment and their mutual relationships. We outline how the connections' patterns shape the organization of the networks by showing the role of the members within the system. By means of a case study of a transnational initiative aiming to support the technology transfer within European countries, we analyse the application of the network science approach, giving evidence of its relative implications
On the statistical description of the inbound air traffic over Heathrow airport
We present a model to describe the inbound air traffic over a congested hub.
We show that this model gives a very accurate description of the traffic by the
comparison of our theoretical distribution of the queue with the actual
distribution observed over Heathrow airport. We discuss also the robustness of
our model
A Small World of Bad Guys: Investigating the Behavior of Hacker Groups in Cyber-Attacks
This paper explores the behaviour of malicious hacker groups operating in
cyberspace and how they organize themselves in structured networks. To better
understand these groups, the paper uses Social Network Analysis (SNA) to
analyse the interactions and relationships among several malicious hacker
groups. The study uses a tested dataset as its primary source, providing an
empirical analysis of the cooperative behaviours exhibited by these groups. The
study found that malicious hacker groups tend to form close-knit networks where
they consult, coordinate with, and assist each other in carrying out their
attacks. The study also identified a "small world" phenomenon within the
population of malicious actors, which suggests that these groups establish
interconnected relationships to facilitate their malicious operations. The
small world phenomenon indicates that the actor-groups are densely connected,
but they also have a small number of connections to other groups, allowing for
efficient communication and coordination of their activities
Measuring network resilience through connection patterns
Networks are at the core of modeling many engineering contexts, mainly in the
case of infrastructures and communication systems. The resilience of a network,
which is the property of the system capable of absorbing external shocks, is
then of paramount relevance in the applications. This paper deals with this
topic by advancing a theoretical proposal for measuring the resilience of a
network. The proposal is based on the study of the shocks propagation along the
patterns of connections among nodes. The theoretical model is tested on the
real-world instances of two important airport systems in the US air traffic
network; Illinois (including the hub of Chicago) and New York states (with JFK
airport).Comment: Keywords: networks; resilience; paths; weighted arcs; air traffic
system
Network-based principles of entrepreneurial ecosystems: a case study of a start-up network.
Entrepreneurial ecosystems are wealthy environments in which entrepreneurs, firms, and governments can operate frictionless, contributing to innovation and economic growth. The investigation of the structure of such systems is an open issue. We provide insights on this aspect through the formulation of seven network-based principles associating specific network metrics to distinct structural features of entrepreneurial ecosystems. In this way, we aim to support the measurement of the structural characteristics of an entrepreneurial ecosystem and the design of policy interventions in case of unmet properties. The proposed methodology is applied to an original network built on the relationships occurring on Twitter among 612 noteworthy start-ups from seven different European countries. This is a novel way to conceptualize entrepreneurial ecosystems considering online interactions. Thus, this work represents a first attempt to analyze the structure of entrepreneurial ecosystems considering their network architecture to guide policy-making decisions. Our results suggest a partial ecosystem-like nature of the analyzed network, providing evidence about possible policy recommendations
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