Implicit graph neural networks have gained popularity in recent years as they
capture long-range dependencies while improving predictive performance in
static graphs. Despite the tussle between performance degradation due to the
oversmoothing of learned embeddings and long-range dependency being more
pronounced in dynamic graphs, as features are aggregated both across
neighborhood and time, no prior work has proposed an implicit graph neural
model in a dynamic setting. In this paper, we present Implicit Dynamic Graph
Neural Network (IDGNN) a novel implicit neural network for dynamic graphs which
is the first of its kind. A key characteristic of IDGNN is that it demonstrably
is well-posed, i.e., it is theoretically guaranteed to have a fixed-point
representation. We then demonstrate that the standard iterative algorithm often
used to train implicit models is computationally expensive in our dynamic
setting as it involves computing gradients, which themselves have to be
estimated in an iterative manner. To overcome this, we pose an equivalent
bilevel optimization problem and propose an efficient single-loop training
algorithm that avoids iterative computation by maintaining moving averages of
key components of the gradients. We conduct extensive experiments on real-world
datasets on both classification and regression tasks to demonstrate the
superiority of our approach over the state-of-the-art baselines. We also
demonstrate that our bi-level optimization framework maintains the performance
of the expensive iterative algorithm while obtaining up to \textbf{1600x}
speed-up.

Implicit Dynamic Graph Neural Network (IDGNN) 是一种针对动态图的新型隐式神经网络模型，通过提出一个等价的双层优化问题和一种高效的单循环训练算法来克服计算上的困难，以在分类和回归任务中展现出优于现有模型的性能，并在性能保持的同时获得高达 1600 倍的加速。

高效有效的隐式动态图神经网络

Efficient and Effective Implicit Dynamic Graph Neural Network

The ability to reliably predict the future quality of a wireless channel, as
seen by the media access control layer, is a key enabler to improve performance
of future industrial networks that do not rely on wires. Knowing in advance how
much channel behavior may change can speed up procedures for adaptively
selecting the best channel, making the network more deterministic, reliable,
and less energy-hungry, possibly improving device roaming capabilities at the
same time.
To this aim, popular approaches based on moving averages and regression were
compared, using multiple key performance indicators, on data captured from a
real Wi-Fi setup. Moreover, a simple technique based on a linear combination of
outcomes from different techniques was presented and analyzed, to further
reduce the prediction error, and some considerations about lower bounds on
achievable errors have been reported. We found that the best model is the
exponential moving average, which managed to predict the frame delivery ratio
with a 2.10\% average error and, at the same time, has lower computational
complexity and memory consumption than the other models we analyzed.

为提高未来工业网络的性能和设备漫游能力，该研究比较了基于移动平均和回归的多个方法，发现指数移动平均是最佳模型，可在预测帧传输比时以平均误差为 2.10% 并具有较低计算复杂度和内存消耗。

利用移动平均和回归模型预测无线信道质量

Predicting Wireless Channel Quality by means of Moving Averages and  Regression Models

In stochastic multi-armed bandits, the reward distribution of each arm is
assumed to be stationary. This assumption is often violated in practice (e.g.,
in recommendation systems), where the reward of an arm may change whenever is
selected, i.e., rested bandit setting. In this paper, we consider the
non-parametric rotting bandit setting, where rewards can only decrease. We
introduce the filtering on expanding window average (FEWA) algorithm that
constructs moving averages of increasing windows to identify arms that are more
likely to return high rewards when pulled once more. We prove that for an
unknown horizon $T$, and without any knowledge on the decreasing behavior of
the $K$ arms, FEWA achieves problem-dependent regret bound of
$\widetilde{\mathcal{O}}(\log{(KT)}),$ and a problem-independent one of
$\widetilde{\mathcal{O}}(\sqrt{KT})$. Our result substantially improves over
the algorithm of Levine et al. (2017), which suffers regret
$\widetilde{\mathcal{O}}(K^{1/3}T^{2/3})$. FEWA also matches known bounds for
the stochastic bandit setting, thus showing that the rotting bandits are not
harder. Finally, we report simulations confirming the theoretical improvements
of FEWA.

本文研究了非参数腐败赌博机算法的问题，提出了一种基于增长窗口平均数的过滤算法 FEWA，用于识别在一次推动后更可能返回高回报的臂。证明了 FEWA 算法在不知道臂的下降行为的情况下，可以实现与扩展周期吻合的回归界限。通过仿真实验也证明了 FEWA 算法的理论改进。