Walks on Apollonian networks

We carry out comparative studies of random walks on deterministic Apollonian networks (DANs) and random Apollonian networks (RANs). We perform computer simulations for the mean first passage time, the average return time, the mean-square displacement, and the network coverage for unrestricted random walk. The diffusions both on DANs and RANs are proved to be sublinear. The search efficiency for walks with various strategies and the influence of the topology of underlying networks on the dynamics of walks are discussed. Contrary to one's intuition, it is shown that the self-avoiding random walk, which has been verified as an optimal strategy for searching on scale-free and small-world networks, is not the best strategy for the DAN in the thermodynamic limit.Comment: 5 pages, 4 figure

A Unified Framework for the Pareto Law and Matthew Effect using Scale-Free Networks

We investigate the accumulated wealth distribution by adopting evolutionary games taking place on scale-free networks. The system self-organizes to a critical Pareto distribution (1897) of wealth $P(m)\sim m^{-(v+1)}$ with $1.6 < v <2.0$ (which is in agreement with that of U.S. or Japan). Particularly, the agent's personal wealth is proportional to its number of contacts (connectivity), and this leads to the phenomenon that the rich gets richer and the poor gets relatively poorer, which is consistent with the Matthew Effect present in society, economy, science and so on. Though our model is simple, it provides a good representation of cooperation and profit accumulation behavior in economy, and it combines the network theory with econophysics.Comment: 5 pages, 8 figure

Urban traffic from the perspective of dual graph

In this paper, urban traffic is modeled using dual graph representation of urban transportation network where roads are mapped to nodes and intersections are mapped to links. The proposed model considers both the navigation of vehicles on the network and the motion of vehicles along roads. The road's capacity and the vehicle-turning ability at intersections are naturally incorporated in the model. The overall capacity of the system can be quantified by a phase transition from free flow to congestion. Simulation results show that the system's capacity depends greatly on the topology of transportation networks. In general, a well-planned grid can hold more vehicles and its overall capacity is much larger than that of a growing scale-free network.Comment: 7 pages, 10 figure

On the complexity of color-avoiding site and bond percolation

The mathematical analysis of robustness and error-tolerance of complex networks has been in the center of research interest. On the other hand, little work has been done when the attack-tolerance of the vertices or edges are not independent but certain classes of vertices or edges share a mutual vulnerability. In this study, we consider a graph and we assign colors to the vertices or edges, where the color-classes correspond to the shared vulnerabilities. An important problem is to find robustly connected vertex sets: nodes that remain connected to each other by paths providing any type of error (i.e. erasing any vertices or edges of the given color). This is also known as color-avoiding percolation. In this paper, we study various possible modeling approaches of shared vulnerabilities, we analyze the computational complexity of finding the robustly (color-avoiding) connected components. We find that the presented approaches differ significantly regarding their complexity.Comment: 14 page

Long-term monitoring of the TeV emission from Mrk 421 with the ARGO-YBJ experiment

ARGO-YBJ is an air shower detector array with a fully covered layer of resistive plate chambers. It is operated with a high duty cycle and a large field of view. It continuously monitors the northern sky at energies above 0.3 TeV. In this paper, we report a long-term monitoring of Mrk 421 over the period from 2007 November to 2010 February. This source was observed by the satellite-borne experiments Rossi X-ray Timing Explorer and Swift in the X-ray band. Mrk 421 was especially active in the first half of 2008. Many flares are observed in both X-ray and gamma-ray bands simultaneously. The gamma-ray flux observed by ARGO-YBJ has a clear correlation with the X-ray flux. No lag between the X-ray and gamma-ray photons longer than 1 day is found. The evolution of the spectral energy distribution is investigated by measuring spectral indices at four different flux levels. Hardening of the spectra is observed in both X-ray and gamma-ray bands. The gamma-ray flux increases quadratically with the simultaneously measured X-ray flux. All these observational results strongly favor the synchrotron self-Compton process as the underlying radiative mechanism.Comment: 30 pages, 8 figure

Search for Gamma Ray Bursts with the Argo-YBJ Detector in Scaler Mode

We report on the search for Gamma Ray Bursts (GRBs) in the energy range 1-100 GeV in coincidence with the prompt emission detected by satellites using the Astrophysical Radiation with Ground-based Observatory at YangBaJing (ARGO-YBJ) air shower detector. Thanks to its mountain location (Yangbajing, Tibet, P.R. China, 4300 m a.s.l.), active surface (about 6700 m**2 of Resistive Plate Chambers), and large field of view (about 2 sr, limited only by the atmospheric absorption), the ARGO-YBJ air shower detector is particularly suitable for the detection of unpredictable and short duration events such as GRBs. The search is carried out using the "single particle technique", i.e. counting all the particles hitting the detector without measurement of the energy and arrival direction of the primary gamma rays. Between 2004 December 17 and 2009 April 7, 81 GRBs detected by satellites occurred within the field of view of ARGO-YBJ (zenith angle < 45 deg). It was possible to examine 62 of these for >1 GeV counterpart in the ARGO-YBJ data finding no statistically significant emission. With a lack of detected spectra in this energy range fluence upper limits are profitable, especially when the redshift is known and the correction for the extragalactic absorption can be considered. The obtained fluence upper limits reach values as low as 10**{-5} erg cm**{-2} in the 1-100 GeV energy region. Besides this individual search for a higher energy counterpart, a statistical study of the stack of all the GRBs both in time and in phase was made, looking for a common feature in the GRB high energy emission. No significant signal has been detected.Comment: accepted for publication in Ap

Discovering universal statistical laws of complex networks

Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their generalisation power, which we identify with large structural variability and absence of constraints imposed by the construction scheme. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This allows, for instance, to infer global features from local ones using regression models trained on networks with high generalisation power. Our results confirm and extend previous findings regarding the synchronisation properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks with good approximation. Finally, we demonstrate on three different data sets (C. elegans' neuronal network, R. prowazekii's metabolic network, and a network of synonyms extracted from Roget's Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models

Artificial Neural Network Inference (ANNI): A Study on Gene-Gene Interaction for Biomarkers in Childhood Sarcomas

Objective: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI). Method: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs) dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model. Results: Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS); FCGRT and OLFM1 in Ewing’s sarcoma (EWS)) suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen. Conclusions: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas

Tag-Aware Recommender Systems: A State-of-the-art Survey

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related works and future challenges of tag-aware recommendation algorithms.Comment: 19 pages, 3 figure