70 research outputs found
Uncovering Offshore Financial Centers: Conduits and Sinks in the Global Corporate Ownership Network
Multinational corporations use highly complex structures of parents and
subsidiaries to organize their operations and ownership. Offshore Financial
Centers (OFCs) facilitate these structures through low taxation and lenient
regulation, but are increasingly under scrutiny, for instance for enabling tax
avoidance. Therefore, the identification of OFC jurisdictions has become a
politicized and contested issue. We introduce a novel data-driven approach for
identifying OFCs based on the global corporate ownership network, in which over
98 million firms (nodes) are connected through 71 million ownership relations.
This granular firm-level network data uniquely allows identifying both
sink-OFCs and conduit-OFCs. Sink-OFCs attract and retain foreign capital while
conduit-OFCs are attractive intermediate destinations in the routing of
international investments and enable the transfer of capital without taxation.
We identify 24 sink-OFCs. In addition, a small set of five countries -- the
Netherlands, the United Kingdom, Ireland, Singapore and Switzerland -- canalize
the majority of corporate offshore investment as conduit-OFCs. Each conduit
jurisdiction is specialized in a geographical area and there is significant
specialization based on industrial sectors. Against the idea of OFCs as exotic
small islands that cannot be regulated, we show that many sink and conduit-OFCs
are highly developed countries
The anatomy of a population-scale social network
Large-scale human social network structure is typically inferred from digital
trace samples of online social media platforms or mobile communication data.
Instead, here we investigate the social network structure of a complete
population, where people are connected by high-quality links sourced from
administrative registers of family, household, work, school, and next-door
neighbors. We examine this multilayer social opportunity structure through
three common concepts in network analysis: degree, closure, and distance.
Findings present how particular network layers contribute to presumably
universal scale-free and small-world properties of networks. Furthermore, we
suggest a novel measure of excess closure and apply this in a life-course
perspective to show how the social opportunity structure of individuals varies
along age, socio-economic status, and education level. Our work provides new
entry points to understand individual socio-economic failure and success as
well as persistent societal problems of inequality and segregation
A large-scale longitudinal structured dataset of the dark web cryptomarket Evolution (2014-2015)
Dark Web Marketplaces (DWM) facilitate the online trade of illicit goods. Due
to the illicit nature of these marketplaces, quality datasets are scarce and
difficult to produce. The Dark Net Market archives (2015) presented raw scraped
source files crawled from a selection of DWMs, including Evolution. Here, we
present, specifically for the Evolution DWM, a structured dataset extracted
from Dark Net Market archive data. Uniquely, many of the data quality issues
inherent to crawled data are resolved. The dataset covers over 500 thousand
forum posts and over 80 thousand listings, providing data on forums, topics,
posts, forum users, market vendors, listings, and more. Additionally, we
present temporal weighted communication networks extracted from this data. The
presented dataset provides easy access to a high quality DWM dataset to
facilitate the study of criminal behaviour and communication on such DWMs,
which may provide a relevant source of knowledge for researchers across
disciplines, from social science to law to network science.Comment: 19 pages, 5 figure
Inverse estimation of the transfer velocity of money
Monitoring the money supply is an important prerequisite for conducting sound
monetary policy, yet monetary indicators are conventionally estimated in
aggregate. This paper proposes a new methodology that is able to leverage
micro-level transaction data from real-world payment systems. We apply a novel
computational technique to measure the durations for which money is held in
individual accounts, and compute the transfer velocity of money from its
inverse. Our new definition reduces to existing definitions under conventional
assumptions. However, inverse estimation remains suitable for payment systems
where the total balance fluctuates and spending patterns change in time. Our
method is applied to study Sarafu, a small digital community currency in Kenya,
where transaction data is available from 25 January 2020 to 15 June 2021. We
find that the transfer velocity of Sarafu was higher than it would seem, in
aggregate, because not all units of Sarafu remained in active circulation.
Moreover, inverse estimation reveals strong heterogineities and enables
comparisons across subgroups of spenders. Some units of Sarafu were held for
minutes, others for months, and spending patterns differed across communities
using Sarafu. The rate of circulation and the effective balance of Sarafu
changed substantially over time, as these communities experienced economic
disruptions related to the COVID-19 pandemic and seasonal food insecurity.
These findings contribute to a growing body of literature documenting the
heterogeneous patterns underlying headline macroeconomic indicators and their
relevance for policy. Inverse estimation may be especially useful in studying
the response of spenders to targeted monetary operations
Circulation of a digital community currency
Circulation is the characteristic feature of successful currency systems,
from community currencies to cryptocurrencies to national currencies. In this
paper, we propose a network analysis methodology for studying circulation given
a system's digital transaction records. This is applied to Sarafu, a digital
community currency active in Kenya over a period that saw considerable economic
disruption due to the COVID-19 pandemic. Representing Sarafu as a network of
monetary flow among the 40,000 users reveals meaningful patterns at multiple
scales. Circulation was highly modular, geographically localized, and occurring
among users with diverse livelihoods. Network centrality highlights women's
participation, early adopters, and the especially prominent role of
community-based financial institutions. These findings have concrete
implications for humanitarian and development policy, helping articulate when
community currencies might best support interventions in marginalized areas.
Overall, networks of monetary flow allow for studying circulation within
digital currency systems at a striking level of detail
Early warning signals for predicting cryptomarket vendor success using dark net forum networks
In this work we focus on identifying key players in dark net cryptomarkets.
Law enforcement aims to disrupt criminal activity conducted through these
markets by targeting key players vital to the market's existence and success.
We particularly focus on detecting successful vendors responsible for the
majority of illegal trade. Our methodology aims to uncover whether the task of
key player identification should center around plainly measuring user and forum
activity, or that it requires leveraging specific patterns of user
communication. We focus on a large-scale dataset from the Evolution
cryptomarket, which we model as an evolving communication network. While user
and forum activity measures are useful for identifying the most successful
vendors, we find that betweenness centrality additionally identifies those with
lesser activity. But more importantly, analyzing the forum data over time, we
find evidence that attaining a high betweenness score comes before vendor
success. This suggests that the proposed network-driven approach of modelling
user communication might prove useful as an early warning signal for key player
identification
Beyond the ego network: The effect of distant connections on node anonymity
Ensuring privacy of individuals is of paramount importance to social network
analysis research. Previous work assessed anonymity in a network based on the
non-uniqueness of a node's ego network. In this work, we show that this
approach does not adequately account for the strong de-anonymizing effect of
distant connections. We first propose the use of d-k-anonymity, a novel measure
that takes knowledge up to distance d of a considered node into account.
Second, we introduce anonymity-cascade, which exploits the so-called
infectiousness of uniqueness: mere information about being connected to another
unique node can make a given node uniquely identifiable. These two approaches,
together with relevant "twin node" processing steps in the underlying graph
structure, offer practitioners flexible solutions, tunable in precision and
computation time. This enables the assessment of anonymity in large-scale
networks with up to millions of nodes and edges. Experiments on graph models
and a wide range of real-world networks show drastic decreases in anonymity
when connections at distance 2 are considered. Moreover, extending the
knowledge beyond the ego network with just one extra link often already
decreases overall anonymity by over 50%. These findings have important
implications for privacy-aware sharing of sensitive network data
The promise and perils of using big data in the study of corporate networks: problems, diagnostics and fixes
Network data on connections among corporate actors and entities – for instance through co-ownership ties or elite social networks – is increasingly available to researchers interested in probing many important questions related to the study of modern capitalism. We discuss the promise and perils of using Big Corporate Network Data (BCND) given the analytical challenges associated with the nature of the subject matter, variable data quality, and other problems associated with currently available data at this scale. We propose a standard process for how researchers can deal with BCND problems. While acknowledging that different research questions require different approaches to data quality, we offer a schematic platform that researchers can follow to make informed and intelligent decisions about BCND issues and address these issues through a specific work-flow procedure. Within each step in this procedure, we provide a set of best practices for how to identify, resolve, and minimize BCND problems that arise
Ranking of Fuzzy Similar Faces Using Relevance Matrix and Aggregation Operators
AbstractIn perception based imaging, Sketching With Words (SWW) is a well-established methodology in which the objects of computation are fuzzy geometric objects (f-objects).The problem of facial imaging of criminal on the basis of onlooker statement is not lack of method and measures but the modeling of onlooker(s) mind set. Because the onlooker has to give statements about different human face parts like forehead, eyes, nose, and chin etc.The concept of fuzzy similarity (f-similarity) and proper aggregation of components of face may provide more flexibility to onlooker(s). In proposed work onlooker(s) statement is recorded. Thereafter it is compared with existing statements. The f-similarity with different faces in database is estimated by using ‘as many as possible’ linguistic quantifier. Three types of constraints over size of parts of face ‘small’, ‘medium’, and ‘large’ are considered. Possibilistic constraints with linguistic hedges and negation operator like ‘very long’, ‘not long’, ‘not very long’ etc. are used. Moreover we have generated ranking of alike faces in decreasing order by using the concepts of f-similarity and relevance matrix
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