Centrality analysis has been a very important research topic in online social networks. Traditionally, centrality analysis is carried out for a specific network. In this article, we conduct centrality analysis in random networks. For this, we consider the class of d-regular directed acyclic random networks generated by the configuration model. We consider two centralities: the PageRank and the in-component centrality (defined as the size of the in-component of a node). For the PageRank, we derive a closed-form solution in the complete directed acyclic network and show that it can be used as an approximation in a d-regular directed acyclic random network. For the in-component centrality, we derive upper bounds for its first moment and its second moment in a d-regular directed acyclic random network. These results have interesting applications in top-k recommendations. In particular, we propose a qualifying round algorithm that outputs a set of nodes to include the top-k nodes with high probability. Various experiments are conducted to show the effectiveness of the algorithm.

In this paper, we propose a unified framework for the residual learning and random forest regression for social media prediction task. Given a post including photo and its social information, the primary goal is to predict the view count of the post. In this regression problem, we first predict the view count based on random forest regressor for the social information. Since regressor tends to learn a relative soothingness model to avoid overfitting, the extreme high/low view counts of the poses are hard to predict. We solve this problem by using residual learning to refine the prediction. Based on this initial prediction, the residual value of the prediction and its ground truth is calculated. Then, the image and its social information will feed to 13-layers ResNet to predict the residual value to compensate the initial prediction for extreme high/low view counts. Experiments show that the performance of the proposed method significantly outperforms other methods.

In this paper, we study the problem of exact emulation of two types of optical queues: (i)N-to-1 output-buffered multiplexers with variable length bursts, and (ii) N times N output-buffered switches with variable length bursts. For both queues, the delay of a packet (in a burst) is known upon its arrival. As such, one can emulate such queues by finding a delay path that yields the exact delay for each packet. For emulating the delay of a packet in such queues, in this paper we consider a multistage feedforward network with optical crossbar switches and fiber delay lines (SDL). For any fixed delay d, there exist multiple delay paths in such a network. A delay path is feasible if it satisfies the following three constraints: (i) conflict constraint: no more than one packet can be scheduled at the same input/output ports of each crossbar switch at the same time, (ii) causality constraint: no packet can be scheduled before its arrival, and (iii) strong contiguity constraint: packets in the same burst should be routed through any fiber delay lines contiguously. By the worst case analysis, we find sufficient conditions for the numbers of delay lines needed in each stage of such a feedforward network to achieve exact emulation of both queues. For N-to-1 output-buffered multiplexers, our sufficient conditions are also necessary when each burst contains exactly one packet. By computer simulation, we also show that the number of delay lines in each stage can be greatly reduced due to statistical multiplexing gain.