104 research outputs found
Heterogeneous network with distance dependent connectivity
Abstract.: We investigate a network model based on an infinite regular square lattice embedded in the Euclidean plane where the node connection probability is given by the geometrical distance of nodes. We show that the degree distribution in the basic model is sharply peaked around its mean value. Since the model was originally developed to mimic the social network of acquaintances, to broaden the degree distribution we propose its generalization. We show that when heterogeneity is introduced to the model, it is possible to obtain fat tails of the degree distribution. Meanwhile, the small-world phenomenon present in the basic model is not affected. To support our claims, both analytical and numerical results are obtaine
The role of a matchmaker in buyer-vendor interactions
We consider a simple market where a vendor offers multiple variants of a certain product and preferences of both the vendor and potential buyers are heterogeneous and possibly even antagonistic. Optimization of the joint benefit of the vendor and the buyers turns the toy market into a combinatorial matching problem. We compare the optimal solutions found with and without a matchmaker, examine the resulting inequality between the market participants, and study the impact of correlations on the syste
Self-organized model of cascade spreading
Abstract.: We study simultaneous price drops of real stocks and show that for high drop thresholds they follow a power-law distribution. To reproduce these collective downturns, we propose aminimal self-organized model of cascade spreading based on a probabilistic response of the system elements to stress conditions. This model is solvable using the theory of branching processes and the mean-field approximation. For a wide range of parameters, the system is in a critical state and displays apower-law cascade-size distribution similar to the empirically observed one. We further generalize the model to reproduce volatility clustering and other observed properties of real stock
Influence, originality and similarity in directed acyclic graphs
We introduce a framework for network analysis based on random walks on
directed acyclic graphs where the probability of passing through a given node
is the key ingredient. We illustrate its use in evaluating the mutual influence
of nodes and discovering seminal papers in a citation network. We further
introduce a new similarity metric and test it in a simple personalized
recommendation process. This metric's performance is comparable to that of
classical similarity metrics, thus further supporting the validity of our
framework.Comment: 6 pages, 4 figure
Recommendation model based on opinion diffusion
Information overload in the modern society calls for highly efficient
recommendation algorithms. In this letter we present a novel diffusion based
recommendation model, with users' ratings built into a transition matrix. To
speed up computation we introduce a Green function method. The numerical tests
on a benchmark database show that our prediction is superior to the standard
recommendation methods.Comment: 5 pages, 2 figure
Heterogeneity, quality, and reputation in an adaptive recommendation model
Abstract.: Recommender systems help people cope with the problem of information overload. A recently proposed adaptive news recommender model [M. Medo, Y.-C. Zhang, T. Zhou, Europhys. Lett. 88, 38005 (2009)] is based on epidemic-like spreading of news in a social network. By means of agent-based simulations we study a "good get richer” feature of the model and determine which attributes are necessary for a user to play a leading role in the network. We further investigate the filtering efficiency of the model as well as its robustness against malicious and spamming behaviour. We show that incorporating user reputation in the recommendation process can substantially improve the outcom
Diversification and limited information in the Kelly game
Financial markets, with their vast range of different investment
opportunities, can be seen as a system of many different simultaneous games
with diverse and often unknown levels of risk and reward. We introduce
generalizations to the classic Kelly investment game [Kelly (1956)] that
incorporates these features, and use them to investigate the influence of
diversification and limited information on Kelly-optimal portfolios. In
particular we present approximate formulas for optimizing diversified
portfolios and exact results for optimal investment in unknown games where the
only available information is past outcomes.Comment: 11 pages, 4 figure
How to project a bipartite network?
The one-mode projecting is extensively used to compress the bipartite
networks. Since the one-mode projection is always less informative than the
bipartite representation, a proper weighting method is required to better
retain the original information. In this article, inspired by the network-based
resource-allocation dynamics, we raise a weighting method, which can be
directly applied in extracting the hidden information of networks, with
remarkably better performance than the widely used global ranking method as
well as collaborative filtering. This work not only provides a creditable
method in compressing bipartite networks, but also highlights a possible way
for the better solution of a long-standing challenge in modern information
science: How to do personal recommendation?Comment: 7 pages, 4 figure
Heterogeneous network with distance dependent connectivity
We investigate a network model based on an infinite regular square lattice
embedded in the Euclidean plane where the node connection probability is given
by the geometrical distance of nodes. We show that the degree distribution in
the basic model is sharply peaked around its mean value. Since the model was
originally developed to mimic the social network of acquaintances, to broaden
the degree distribution we propose its generalization. We show that when
heterogeneity is introduced to the model, it is possible to obtain fat tails of
the degree distribution. Meanwhile, the small-world phenomenon present in the
basic model is not affected. To support our claims, both analytical and
numerical results are obtained.Comment: 6 pages, 4 figures, minor clarifications and references adde
DNA-PK in human malignant disorders: Mechanisms and implications for pharmacological interventions.
The DNA-PK holoenzyme is a fundamental element of the DNA damage response machinery (DDR), which is responsible for cellular genomic stability. Consequently, and predictably, over the last decades since its identification and characterization, numerous pre-clinical and clinical studies reported observations correlating aberrant DNA-PK status and activity with cancer onset, progression and responses to therapeutic modalities. Notably, various studies have established in recent years the role of DNA-PK outside the DDR network, corroborating its role as a pleiotropic complex involved in transcriptional programs that operate biologic processes as epithelial to mesenchymal transition (EMT), hypoxia, metabolism, nuclear receptors signaling and inflammatory responses. In particular tumor entities as prostate cancer, immense research efforts assisted mapping and describing the overall signaling networks regulated by DNA-PK that control metastasis and tumor progression. Correspondingly, DNA-PK emerges as an obvious therapeutic target in cancer and data pertaining to various pharmacological approaches have been published, largely in context of combination with DNA-damaging agents (DDAs) that act by inflicting DNA double strand breaks (DSBs). Currently, new generation inhibitors are tested in clinical trials. Several excellent reviews have been published in recent years covering the biology of DNA-PK and its role in cancer. In the current article we are aiming to systematically describe the main findings on DNA-PK signaling in major cancer types, focusing on both preclinical and clinical reports and present a detailed current status of the DNA-PK inhibitors repertoire
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