Temporal effects in trend prediction: identifying the most popular nodes in the future

Prediction is an important problem in different science domains. In this paper, we focus on trend prediction in complex networks, i.e. to identify the most popular nodes in the future. Due to the preferential attachment mechanism in real systems, nodes' recent degree and cumulative degree have been successfully applied to design trend prediction methods. Here we took into account more detailed information about the network evolution and proposed a temporal-based predictor (TBP). The TBP predicts the future trend by the node strength in the weighted network with the link weight equal to its exponential aging. Three data sets with time information are used to test the performance of the new method. We find that TBP have high general accuracy in predicting the future most popular nodes. More importantly, it can identify many potential objects with low popularity in the past but high popularity in the future. The effect of the decay speed in the exponential aging on the results is discussed in detail

Characterizing Self-Developing Biological Neural Networks: A First Step Towards their Application To Computing Systems

Carbon nanotubes are often seen as the only alternative technology to silicon transistors. While they are the most likely short-term one, other longer-term alternatives should be studied as well. While contemplating biological neurons as an alternative component may seem preposterous at first sight, significant recent progress in CMOS-neuron interface suggests this direction may not be unrealistic; moreover, biological neurons are known to self-assemble into very large networks capable of complex information processing tasks, something that has yet to be achieved with other emerging technologies. The first step to designing computing systems on top of biological neurons is to build an abstract model of self-assembled biological neural networks, much like computer architects manipulate abstract models of transistors and circuits. In this article, we propose a first model of the structure of biological neural networks. We provide empirical evidence that this model matches the biological neural networks found in living organisms, and exhibits the small-world graph structure properties commonly found in many large and self-organized systems, including biological neural networks. More importantly, we extract the simple local rules and characteristics governing the growth of such networks, enabling the development of potentially large but realistic biological neural networks, as would be needed for complex information processing/computing tasks. Based on this model, future work will be targeted to understanding the evolution and learning properties of such networks, and how they can be used to build computing systems

Green Hybrid Satellite Terrestrial Networks: Fundamental Trade-Off Analysis

With the worldwide evolution of 4G generation and revolution in the information and communications technology(ICT) field to meet the exponential increase of mobile data traffic in the 2020 era, the hybrid satellite and terrestrial network based on the soft defined features is proposed from a perspective of 5G. In this paper, an end-to-end architecture of hybrid satellite and terrestrial network under the control and user Plane (C/U) split concept is studied and the performances are analysed based on stochastic geometry. The relationship between spectral efficiency (SE) and energy efficiency (EE) is investigated, taking consideration of overhead costs, transmission and circuit power, backhaul of gateway (GW), and density of small cells. Numerical results show that, by optimizing the key parameters, the hybrid satellite and terrestrial network can achieve nearly 90% EE gain with only 3% SE loss in relative dense networks, and achieve both higher EE and SE gain (20% and 5% respectively) in sparse networks toward the future 5G green communication networks

Mobility-aware QoS assurance in software-defined radio access networks: an analytical study

Software-defined networking (SDN) has gained a tremendous attention in the recent years, both in academia and industry. This revolutionary networking paradigm is an attempt to bring the advances in computer science and software engineering into the information and communications technology (ICT) domain. The aim of these efforts is to pave the way for completely programmable networks and control-data plane separation. Recent studies on feasibility and applicability of SDN concepts in cellular networks show very promising results and this trend will most likely continue in near future. In this work, we study the benefits of SDN on the radio resource management (RRM) of future-generation cellular networks. Our considered cellular network architecture is in line with the recently proposed Long-Term Evolution (LTE) Release 12 concepts, such as user/control plane split, heterogeneous networks (HetNets) environment, and network densification through deployment of small cells. In particular, the aim of our RRM scheme is to enable the macro base station (BS) to efficiently allocate radio resources for small cell BSs in order to assure quality-of-service (QoS) of moving users/vehicles during handovers. We develop an approximate, but very time- and space-efficient algorithm for radio resource allocation within a HetNet. Experiments on commodity hardware show algorithm running times in the order of a few seconds, thus making it suitable even in cases of fast moving users/vehicles. We also confirm a good accuracy of our proposed algorithm by means of computer simulations

Training Input-Output Recurrent Neural Networks through Spectral Methods

We consider the problem of training input-output recurrent neural networks (RNN) for sequence labeling tasks. We propose a novel spectral approach for learning the network parameters. It is based on decomposition of the cross-moment tensor between the output and a non-linear transformation of the input, based on score functions. We guarantee consistent learning with polynomial sample and computational complexity under transparent conditions such as non-degeneracy of model parameters, polynomial activations for the neurons, and a Markovian evolution of the input sequence. We also extend our results to Bidirectional RNN which uses both previous and future information to output the label at each time point, and is employed in many NLP tasks such as POS tagging

Impact of users inter-contact times on information dissemination in pervasive social networks

It is commonly perceived that the design principles of the future Internet might be drastically different from today. It is natural to ask what will be the impact of such evolution on the design of future Online Social Networks (OSNs). There is evidence that human social networks may be invariant with respect to the underlying online technology supporting them. Furthermore, the increasing pervasiveness of communication technologies is likely to enable any two users to communicate anytime and anywhere. Thus, a possible evolution of OSN design could map directly the structure of human social networks, and build future OSN services on top of a network whose edges represent "communication channels" between users sharing social relationships, and activated when they interact because of their social ties. In this paper we look, in the perspective of future OSN designed according to this concept, at how the patterns of interactions between people in human social networks impact on information dissemination properties. Based on well-established theories from the anthropology field, we study the properties of inter-contact times between users, i.e. the time between successive communication opportunities. This is a crucial feature for information dissemination, as previous results obtained in a conceptually similar environment have shown that the distribution of inter-contact times determines the convergence properties of information diffusion protocols. In the paper we investigate, by analysis, simulation and experimental results, the impact of different users interaction patterns on the properties of inter-contact times and, thus, on the convergence properties of information dissemination protocols