Attributed event sequences are commonly encountered in practice. A recent
research line focuses on incorporating neural networks with the statistical
model -- marked point processes, which is the conventional tool for dealing
with attributed event sequences. Neural marked point processes possess good
interpretability of probabilistic models as well as the representational power
of neural networks. However, we find that performance of neural marked point
processes is not always increasing as the network architecture becomes more
complicated and larger, which is what we call the performance saturation
phenomenon. This is due to the fact that the generalization error of neural
marked point processes is determined by both the network representational
ability and the model specification at the same time. Therefore we can draw two
major conclusions: first, simple network structures can perform no worse than
complicated ones for some cases; second, using a proper probabilistic
assumption is as equally, if not more, important as improving the complexity of
the network. Based on this observation, we propose a simple graph-based network
structure called GCHP, which utilizes only graph convolutional layers, thus it
can be easily accelerated by the parallel mechanism. We directly consider the
distribution of interarrival times instead of imposing a specific assumption on
the conditional intensity function, and propose to use a likelihood ratio loss
with a moment matching mechanism for optimization and model selection.
Experimental results show that GCHP can significantly reduce training time and
the likelihood ratio loss with interarrival time probability assumptions can
greatly improve the model performance.

将神经网络与统计模型相结合的神经标记点进程有时会遇到性能饱和现象。为了解决这个问题，本文提出了一种简单的基于图形的结构，使用图卷积层，直接考虑到达时间分布，采用似然比损失优化模型，实验结果表明，这种方法可以显著减少训练时间，同时提高模型性能。

神经标记点过程中的性能饱和缓解：架构和损失函数

Mitigating Performance Saturation in Neural Marked Point Processes:  Architectures and Loss Functions

We propose a combination of a variational autoencoder and a transformer based
model which fully utilises graph convolutional and graph pooling layers to
operate directly on graphs. The transformer model implements a novel node
encoding layer, replacing the position encoding typically used in transformers,
to create a transformer with no position information that operates on graphs,
encoding adjacent node properties into the edge generation process. The
proposed model builds on graph generative work operating on graphs with edge
features, creating a model that offers improved scalability with the number of
nodes in a graph. In addition, our model is capable of learning a disentangled,
interpretable latent space that represents graph properties through a mapping
between latent variables and graph properties. In experiments we chose a
benchmark task of molecular generation, given the importance of both generated
node and edge features. Using the QM9 dataset we demonstrate that our model
performs strongly across the task of generating valid, unique and novel
molecules. Finally, we demonstrate that the model is interpretable by
generating molecules controlled by molecular properties, and we then analyse
and visualise the learned latent representation.

本论文提出了一种结合了变分自动编码器和基于变压器模型的图卷积和图池化层的方法，直接对图进行操作，能够生成具有可解释特性的潜空间和有效生成分子的模型。

生成分子图的图形 VAE 和图形 Transformer 方法

A Graph VAE and Graph Transformer Approach to Generating Molecular  Graphs

The information contained in hierarchical topology, intrinsic to many
networks, is currently underutilised. A novel architecture is explored which
exploits this information through a multiscale decomposition. A dendrogram is
produced by a Girvan-Newman hierarchical clustering algorithm. It is segmented
and fed through graph convolutional layers, allowing the architecture to learn
multiple scale latent space representations of the network, from fine to coarse
grained. The architecture is tested on a benchmark citation network,
demonstrating competitive performance. Given the abundance of hierarchical
networks, possible applications include quantum molecular property prediction,
protein interface prediction and multiscale computational substrates for
partial differential equations.

研究了利用多尺度分解通过层次拓扑信息的新型体系结构，并通过 Girvan-Newman 层次聚类算法生成了一棵树，使得架构可以学习从粗到细的多尺度潜在空间表示，并在基准引文网络上表现出竞争力。