Dániel Kondor

Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network

The possibility to analyze everyday monetary transactions is limited by the scarcity of available data, as this kind of information is usually considered highly sensitive. Present econophysics models are usually employed on presumed random networks of interacting agents, and only some macroscopic properties (e.g. the resulting wealth distribution) are compared to real-world data. In this paper, we analyze Bitcoin, which is a novel digital currency system, where the complete list of transactions is publicly available. Using this dataset, we reconstruct the network of transactions and extract the time and amount of each payment. We analyze the structure of the transaction network by measuring network characteristics over time, such as the degree distribution, degree correlations and clustering. We find that linear preferential attachment drives the growth of the network. We also study the dynamics taking place on the transaction network, i.e. the flow of money. We measure temporal patterns and the wealth accumulation. Investigating the microscopic statistics of money movement, we find that sublinear preferential attachment governs the evolution of the wealth distribution. We report a scaling law between the degree and wealth associated to individual nodes.

Complete Genes May Pass from Food to Human Blood

Our bloodstream is considered to be an environment well separated from the outside world and the digestive tract. According to the standard paradigm large macromolecules consumed with food cannot pass directly to the circulatory system. During digestion proteins and DNA are thought to be degraded into small constituents, amino acids and nucleic acids, respectively, and then absorbed by a complex active process and distributed to various parts of the body through the circulation system. Here, based on the analysis of over 1000 human samples from four independent studies, we report evidence that meal-derived DNA fragments which are large enough to carry complete genes can avoid degradation and through an unknown mechanism enter the human circulation system. In one of the blood samples the relative concentration of plant DNA is higher than the human DNA. The plant DNA concentration shows a surprisingly precise log-normal distribution in the plasma samples while non-plasma (cord blood) control sample was found to be free of plant DNA.

Inferring the interplay between 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.

Dynamics and Structure in Cell Signaling Networks: Off-State Stability and Dynamically Positive Cycles

The signaling system is a fundamental part of the cell, as it regulates essential functions including growth, differentiation, protein synthesis, and apoptosis. A malfunction in this subsystem can disrupt the cell significantly, and is believed to be involved in certain diseases, with cancer being a very important example. While the information available about intracellular signaling networks is constantly growing, and the network topology is actively being analyzed, the modeling of the dynamics of such a system faces difficulties due to the vast number of parameters, which can prove hard to estimate correctly. As the functioning of the signaling system depends on the parameters in a complex way, being able to make general statements based solely on the network topology could be especially appealing. We study a general kinetic model of the signaling system, giving results for the asymptotic behavior of the system in the case of a network with only activatory interactions. We also investigate the possible generalization of our results for the case of a more general model including inhibitory interactions too. We find that feedback cycles made up entirely of activatory interactions (which we call dynamically positive) are especially important, as their properties determine whether the system has a stable signal-off state, which is desirable in many situations to avoid autoactivation due to a noisy environment. To test our results, we investigate the network topology in the Signalink database, and find that the human signaling network indeed has only significantly few dynamically positive cycles, which agrees well with our theoretical arguments.

Uncovering Urban Temporal Patterns from Geo-Tagged Photography

We live in a world where digital trails of different forms of human activities compose big urban data, allowing us to detect many aspects of how people experience the city in which they live or come to visit. In this study we propose to enhance urban planning by taking into a consideration individual preferences using information from an unconventional big data source: dataset of geo-tagged photographs that people take in cities which we then use as a measure of urban attractiveness. We discover and compare a temporal behavior of residents and visitors in ten most photographed cities in the world. Looking at the periodicity in urban attractiveness, the results show that the strongest periodic patterns for visitors are usually weekly or monthly. Moreover, by dividing cities into two groups based on which continent they belong to (i.e., North America or Europe), it can be concluded that unlike European cities, behavior of visitors in the US cities in general is similar to the behavior of their residents. Finally, we apply two indices, called “dilatation attractiveness index” and “dilatation index”, to our dataset which tell us the spatial and temporal attractiveness pulsations in the city. The proposed methodology is not only important for urban planning, but also does support various business and public stakeholder decision processes, concentrated for example around the question how to attract more visitors to the city or estimate the impact of special events organized there.

Lost in the city: revisiting Milgram's experiment in the age of social networks.

As more and more users access social network services from smart devices with GPS receivers, the available amount of geo-tagged information makes repeating classical experiments possible on global scales and with unprecedented precision. Inspired by the original experiments of Milgram, we simulated message routing within a representative sub-graph of the network of Twitter users with about 6 million geo-located nodes and 122 million edges. We picked pairs of users from two distant metropolitan areas and tried to find a route between them using local geographic information only; our method was to forward messages to a friend living closest to the target. We found that the examined network is navigable on large scales, but navigability breaks down at the city scale and the network becomes unnavigable on intra-city distances. This means that messages usually arrived to the close proximity of the target in only 3–6 steps, but only in about 20% of the cases was it possible to find a route all the way to the recipient, in spite of the network being connected. This phenomenon is supported by the distribution of link lengths; on larger scales the distribution behaves approximately as , which was found earlier by Kleinberg to allow efficient navigation, while on smaller scales, a fractal structure becomes apparent. The intra-city correlation dimension of the network was found to be , less than the dimension of the distribution of the population.

Efficient classification of billions of points into complex geographic regions using hierarchical triangular mesh

We present a case study about the spatial indexing and regional classification of billions of geographic coordinates from geo-tagged social network data using Hierarchical Triangular Mesh (HTM) implemented for Microsoft SQL Server. Due to the lack of certain features of the HTM library, we use it in conjunction with the GIS functions of SQL Server to significantly increase the efficiency of pre-filtering of spatial filter and join queries. For example, we implemented a new algorithm to compute the HTM tessellation of complex geographic regions and precomputed the intersections of HTM triangles and geographic regions for faster false-positive filtering. With full control over the index structure, HTM-based pre-filtering of simple containment searches outperforms SQL Server spatial indices by a factor of ten and HTM-based spatial joins run about a hundred times faster.

A cell signaling model as a trainable neural nanonetwork

Abstract All cells have to adapt to changing chemical environments. The signaling system reacts to external molecular ‘inputs’ arriving at the receptors by activating cellular responses via transcription factors generating proper proteins as ‘outputs’. The signal transduction network connecting inputs and outputs acts as a molecular computer mimicking a neural network, a ‘chemical brain’ of the cell. The dynamics of concentrations of various signal proteins in the cell are described by continuous kinetic models proposed recently. In this paper we introduce a special neural network model based on the ordinary differential equations of the kinetic processes. We show that supervised learning can be implemented using the delta rule for updating the weights of the molecular neurons. We demonstrate the concept by realizing some of the basic logic gates in the model.

Measuring the dimension of partially embedded networks

Scaling phenomena have been intensively studied during the past decade in the context of complex networks. As part of these works, recently novel methods have appeared to measure the dimension of abstract and spatially embedded networks. In this paper we propose a new dimension measurement method for networks, which does not require global knowledge on the embedding of the nodes, instead it exploits link-wise information (link lengths, link delays or other physical quantities). Our method can be regarded as a generalization of the spectral dimension, that grasps the network’s large-scale structure through local observations made by a random walker while traversing the links. We apply the presented method to synthetic and real-world networks, including road maps, the Internet infrastructure and the Gowalla geosocial network. We analyze the theoretically and empirically designated case when the length distribution of the links has the form P(ρ)∼1/ρ. We show that while previous dimension concepts are not applicable in this case, the new dimension measure still exhibits scaling with two distinct scaling regimes. Our observations suggest that the link length distribution is not sufficient in itself to entirely control the dimensionality of complex networks, and we show that the proposed measure provides information that complements other known measures.

A neural nanonetwork model based on cell signaling molecules

All cells have to adapt to changing chemical environments. The signaling system reacts to external molecular ‘inputs’ arriving at the receptors by activating cellular responses via transcription factors generating proper proteins as ‘outputs’. The signal transduction network connecting inputs and outputs acts as a molecular computer mimicking a neural network, a ‘chemical brain’ of the cell. The dynamics of concentrations of various signal proteins in the cell are described by continuous kinetic models proposed recently. In this paper we introduce a special neural network model based on the ordinary differential equations of the kinetic processes. We show that supervised learning can be implemented using the delta rule for updating the weights of the molecular neurons. We demonstrate the concept by realizing some of the basic logical gates in the model.