Recently, techniques for applying convolutional neural networks to graph-structured data have emerged. Graph convolutional neural networks (GCNNs) have been used to address node and graph classification and matrix completion. Although the performance has been impressive, the current implementations have limited capability to incorporate uncertainty in the graph structure. Almost all GCNNs process a graph as though it is a ground-truth depiction of the relationship between nodes, but often the graphs employed in applications are themselves derived from noisy data or modelling assumptions. Spurious edges may be included; other edges may be missing between nodes that have very strong relationships. In this paper we adopt a Bayesian approach, viewing the observed graph as a realization from a parametric family of random graphs. We then target inference of the joint posterior of the random graph parameters and the node (or graph) labels. We present the Bayesian GCNN framework and develop an iterative learning procedure for the case of assortative mixed-membership stochastic block models. We present the results of experiments that demonstrate that the Bayesian formulation can provide better performance when there are very few labels available during the training process.
This paper introduces a distributed auxiliary particle filter for target tracking in sensor networks. Nodes maintain a shared particle filter by coming to a consensus about the likelihoods associated with each particle using the selective gossip procedure. Selective gossip provides a mechanism to efficiently identify the particles with largest weights and focus communication on sharing these important weights. We demonstrate through simulations that the algorithm performs well; compared to state-of-the-art approaches it either significantly improves the accuracy at the expense of a small increase in communication overhead, or achieves comparable accuracy with much lower communication overhead.
Abstract-This paper presents greedy gossip with eavesdropping (GGE), a new average consensus algorithm for wireless sensor network applications. Consensus algorithms have recently received much attention in the sensor network community because of their simplicity and completely decentralized nature which makes them robust to changes in the network topology and unreliable wireless networking environments. In the sensor network, each node has a measurement value and the aim of average consensus is computing the average of these node values in the absence of a central authority. We prove that GGE converges to the average consensus with probability one. We also illustrate the performance of the algorithm via simulations and conclude that GGE provides a significant performance improvement compared to existing average consensus algorithms such as randomized gossip and geographic gossip.
Abstract-This paper presents greedy gossip with eavesdropping (GGE), a new average consensus algorithm for wireless sensor network applications. Consensus algorithms have recently received much attention in the sensor network community because of their simplicity and completely decentralized nature which makes them robust to changes in the network topology and unreliable wireless networking environments. In the sensor network, each node has a measurement value and the aim of average consensus is computing the average of these node values in the absence of a central authority. We prove that GGE converges to the average consensus with probability one. We also illustrate the performance of the algorithm via simulations and conclude that GGE provides a significant performance improvement compared to existing average consensus algorithms such as randomized gossip and geographic gossip.
Abstract-Active Queue Management (AQM) is used in comr-complicated to implement in real networks. Therefore we puter networks to increase link utilization with less queueing consider simpler PI controllers and switch among them.delays. The fluid flow model of TCP based on delay differentialIn this paper we apply the PI controller proposed in 1118] to a equations supplies the mathematical background for modelling . w the AQM as a feedback system. Recently various PI and PID network with time varying round trip times(RTT). We propose controllers are designed for this feedback system, [7], [18]. In that when RTT (i.e. time delay) varies with time, dividing the this paper, we consider the case for which the Round Trip Time operating range to smaller regions and designing PI controllers (RTT) is time varying and we propose switching resilient PI for each of these regions give better results than using a single controllers using the design method introduced in [18].PI controller for the entire operating range. This proposition
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