6,404 research outputs found
Inferring the interplay of network structure and market effects in Bitcoin
A main focus in economics research is understanding the time series of prices
of goods and assets. While statistical models using only the properties of the
time series itself have been successful in many aspects, we expect to gain a
better understanding of the phenomena involved if we can model the underlying
system of interacting agents. In this article, we consider the history of
Bitcoin, a novel digital currency system, for which the complete list of
transactions is available for analysis. Using this dataset, we reconstruct the
transaction network between users and analyze changes in the structure of the
subgraph induced by the most active users. Our approach is based on the
unsupervised identification of important features of the time variation of the
network. Applying the widely used method of Principal Component Analysis to the
matrix constructed from snapshots of the network at different times, we are
able to show how structural changes in the network accompany significant
changes in the exchange price of bitcoins.Comment: project website: http://www.vo.elte.hu/bitcoi
Network-based indicators of Bitcoin bubbles
The functioning of the cryptocurrency Bitcoin relies on the open availability
of the entire history of its transactions. This makes it a particularly
interesting socio-economic system to analyse from the point of view of network
science. Here we analyse the evolution of the network of Bitcoin transactions
between users. We achieve this by using the complete transaction history from
December 5th 2011 to December 23rd 2013. This period includes three bubbles
experienced by the Bitcoin price. In particular, we focus on the global and
local structural properties of the user network and their variation in relation
to the different period of price surge and decline. By analysing the temporal
variation of the heterogeneity of the connectivity patterns we gain insights on
the different mechanisms that take place during bubbles, and find that hubs
(i.e., the most connected nodes) had a fundamental role in triggering the burst
of the second bubble. Finally, we examine the local topological structures of
interactions between users, we discover that the relative frequency of triadic
interactions experiences a strong change before, during and after a bubble, and
suggest that the importance of the hubs grows during the bubble. These results
provide further evidence that the behaviour of the hubs during bubbles
significantly increases the systemic risk of the Bitcoin network, and discuss
the implications on public policy interventions
A Broad Evaluation of the Tor English Content Ecosystem
Tor is among most well-known dark net in the world. It has noble uses,
including as a platform for free speech and information dissemination under the
guise of true anonymity, but may be culturally better known as a conduit for
criminal activity and as a platform to market illicit goods and data. Past
studies on the content of Tor support this notion, but were carried out by
targeting popular domains likely to contain illicit content. A survey of past
studies may thus not yield a complete evaluation of the content and use of Tor.
This work addresses this gap by presenting a broad evaluation of the content of
the English Tor ecosystem. We perform a comprehensive crawl of the Tor dark web
and, through topic and network analysis, characterize the types of information
and services hosted across a broad swath of Tor domains and their hyperlink
relational structure. We recover nine domain types defined by the information
or service they host and, among other findings, unveil how some types of
domains intentionally silo themselves from the rest of Tor. We also present
measurements that (regrettably) suggest how marketplaces of illegal drugs and
services do emerge as the dominant type of Tor domain. Our study is the product
of crawling over 1 million pages from 20,000 Tor seed addresses, yielding a
collection of over 150,000 Tor pages. We make a dataset of the intend to make
the domain structure publicly available as a dataset at
https://github.com/wsu-wacs/TorEnglishContent.Comment: 11 page
Signed Network Modeling Based on Structural Balance Theory
The modeling of networks, specifically generative models, have been shown to
provide a plethora of information about the underlying network structures, as
well as many other benefits behind their construction. Recently there has been
a considerable increase in interest for the better understanding and modeling
of networks, but the vast majority of this work has been for unsigned networks.
However, many networks can have positive and negative links(or signed
networks), especially in online social media, and they inherently have
properties not found in unsigned networks due to the added complexity.
Specifically, the positive to negative link ratio and the distribution of
signed triangles in the networks are properties that are unique to signed
networks and would need to be explicitly modeled. This is because their
underlying dynamics are not random, but controlled by social theories, such as
Structural Balance Theory, which loosely states that users in social networks
will prefer triadic relations that involve less tension. Therefore, we propose
a model based on Structural Balance Theory and the unsigned Transitive Chung-Lu
model for the modeling of signed networks. Our model introduces two parameters
that are able to help maintain the positive link ratio and proportion of
balanced triangles. Empirical experiments on three real-world signed networks
demonstrate the importance of designing models specific to signed networks
based on social theories to obtain better performance in maintaining signed
network properties while generating synthetic networks.Comment: CIKM 2018: https://dl.acm.org/citation.cfm?id=327174
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