Exploring complex networks by walking on them

We carry out a comparative study on the problem for a walker searching on several typical complex networks. The search efficiency is evaluated for various strategies. Having no knowledge of the global properties of the underlying networks and the optimal path between any two given nodes, it is found that the best search strategy is the self-avoid random walk. The preferentially self-avoid random walk does not help in improving the search efficiency further. In return, topological information of the underlying networks may be drawn by comparing the results of the different search strategies.Comment: 5 pages, 5 figure

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

Efficiency of network structures: The needle in the haystack

The modelling of networks formation has recently became the object of an increasing interest in economics. One of the important issues raised in this literature is the one of networks efficiency. Nevertheless, for non trivial payoff functions, searching for efficient network structures turns out to be a very difficult analytical problem as well as a huge computational task, even for a relatively small number of agents. In this paper, we explore the possibility of using genetic algorithms (GA) techniques for identifying efficient network structures, because the GA have proved their power as a tool for solving complex optimization problems. The robustness of this method in predicting optimal network structures is tested on two simple stylized models introduced by Jackson and Wolinski (1996), for which the efficient networks are known over the whole state space of parameter values.Networks, Optimal network structure, Efficiency, Genetic Algorithms

Learning to Auto Weight: Entirely Data-driven and Highly Efficient Weighting Framework

Example weighting algorithm is an effective solution to the training bias problem, however, most previous typical methods are usually limited to human knowledge and require laborious tuning of hyperparameters. In this paper, we propose a novel example weighting framework called Learning to Auto Weight (LAW). The proposed framework finds step-dependent weighting policies adaptively, and can be jointly trained with target networks without any assumptions or prior knowledge about the dataset. It consists of three key components: Stage-based Searching Strategy (3SM) is adopted to shrink the huge searching space in a complete training process; Duplicate Network Reward (DNR) gives more accurate supervision by removing randomness during the searching process; Full Data Update (FDU) further improves the updating efficiency. Experimental results demonstrate the superiority of weighting policy explored by LAW over standard training pipeline. Compared with baselines, LAW can find a better weighting schedule which achieves much more superior accuracy on both biased CIFAR and ImageNet.Comment: Accepted by AAAI 202

On the performance of densified DVB-H single frequency networks

The broadcasting of TV programmes to mobile phones can be enabled by the newly developed technology called Digital Video Broadcasting-Handheld (DVB-H). Because of the scarcity and cost of frequency resources, frequency reuse needs to be considered when rolling out DVB-H networks. By simulcasting the same content from several transmitters, a Single Frequency Network (SFN) can provide good coverage and good frequency efficiency. In this paper, the performance of densified DVB-H SFN networks is analysed in terms of the coverage probability under different coverage requirements with and without frequency reuse. A dichotomy searching approach is used to determine the optimal cell radius for a cell in a densified DVB-H SFN for a given network topology. Based on the optimal cell radius map and a SFN gain map generated from the simulation results, guidelines are proposed on how to avoid the potential pitfalls in configuring the parameters of a densified DVB-H SFN network and optimise its parameters in terms of minimising the cost of the network for a range of predefined network parameters

A Secure and User Privacy-Preserving Searching Protocol for Peer-to-Peer Networks

File sharing peer-to-peer networks have become quite popular of late as a new paradigm for information exchange among large number of users in the Internet. However, these networks suffer from several problems such as fake content distribution, free riding, whitewashing, poor search scalability, lack of a robust trust model and absence of user privacy protection mechanism. In this paper, a secure and efficient searching scheme for peer-to-peer networks has been proposed that utilizes topology adaptation by constructing an overlay of trusted peers where the neighbors are selected based on their trust ratings and content similarities. While increasing the search efficiency by intelligently exploiting the formation of semantic community structures among the trustworthy peers, the scheme provides a highly reliable module for protecting the privacy of the users and data in the network. Simulation results have demonstrated that the proposed scheme provides efficient searching to good peers while penalizing the malicious peers by increasing their search times

Efficient Aerial Data Collection with UAV in Large-Scale Wireless Sensor Networks

Data collection from deployed sensor networks can be with static sink, ground-based mobile sink, or Unmanned Aerial Vehicle (UAV) based mobile aerial data collector. Considering the large-scale sensor networks and peculiarity of the deployed environments, aerial data collection based on controllable UAV has more advantages. In this paper, we have designed a basic framework for aerial data collection, which includes the following five components: deployment of networks, nodes positioning, anchor points searching, fast path planning for UAV, and data collection from network. We have identified the key challenges in each of them and have proposed efficient solutions. This includes proposal of a Fast Path Planning with Rules (FPPWR) algorithm based on grid division, to increase the efficiency of path planning, while guaranteeing the length of the path to be relatively short. We have designed and implemented a simulation platform for aerial data collection from sensor networks and have validated performance efficiency of the proposed framework based on the following parameters: time consumption of the aerial data collection, flight path distance, and volume of collected data