We present the analysis of the topological graph descriptor Local Degree
Profile (LDP), which forms a widely used structural baseline for graph
classification. Our study focuses on model evaluation in the context of the
recently developed fair evaluation framework, which defines rigorous routines
for model selection and evaluation for graph classification, ensuring
reproducibility and comparability of the results. Based on the obtained
insights, we propose a new baseline algorithm called Local Topological Profile
(LTP), which extends LDP by using additional centrality measures and local
vertex descriptors. The new approach provides the results outperforming or very
close to the latest GNNs for all datasets used. Specifically, state-of-the-art
results were obtained for 4 out of 9 benchmark datasets. We also consider
computational aspects of LDP-based feature extraction and model construction to
propose practical improvements affecting execution speed and scalability. This
allows for handling modern, large datasets and extends the portfolio of
benchmarks used in graph representation learning. As the outcome of our work,
we obtained LTP as a simple to understand, fast and scalable, still robust
baseline, capable of outcompeting modern graph classification models such as
Graph Isomorphism Network (GIN). We provide open-source implementation at
\href{this https URL}{GitHub}.

本文通过使用公平评估框架，对局部度量描述符 (Local Degree Profile) 进行分析，并提出了基于局部中心性量度和局部顶点描述符扩展的新算法 (Local Topological Profile)，其结果在大多数数据集上优于或接近于现代图分类模型 Graph Isomorphism Network (GIN)。而且，研究了 LDP 特征提取和模型构建的计算方面，提出了可行的高效实现方案，并将其开源。

使用本地拓扑特征强化图分类的结构基准线

Strengthening structural baselines for graph classification using Local  Topological Profile

We propose a manager-worker framework based on deep reinforcement learning to
tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP),
\ie~multiple-vehicle TSP with time window and rejections (mTSPTWR), where
customers who cannot be served before the deadline are subject to rejections.
Particularly, in the proposed framework, a manager agent learns to divide
mTSPTWR into sub-routing tasks by assigning customers to each vehicle via a
Graph Isomorphism Network (GIN) based policy network. A worker agent learns to
solve sub-routing tasks by minimizing the cost in terms of both tour length and
rejection rate for each vehicle, the maximum of which is then fed back to the
manager agent to learn better assignments. Experimental results demonstrate
that the proposed framework outperforms strong baselines in terms of higher
solution quality and shorter computation time. More importantly, the trained
agents also achieve competitive performance for solving unseen larger
instances.

提出一种基于深度强化学习的经理 - 工作人员框架来解决一种复杂但非常重要的旅行商问题的变体（即具有时间窗口和拒绝的多车 TSP），其中不能在截止日期前提供服务的客户可能被拒绝。经过实验结果证明，该框架在解决大型问题时具有高质量的解决方案和更短的计算时间。

通过深度强化学习学习解决包含时间窗和拒绝的多个 TSP

Learning to Solve Multiple-TSP with Time Window and Rejections via Deep Reinforcement Learning

Speech emotion recognition (SER) is an essential part of human-computer
interaction. In this paper, we propose an SER network based on a Graph
Isomorphism Network with Weighted Multiple Aggregators (WMA-GIN), which can
effectively handle the problem of information confusion when neighbour nodes'
features are aggregated together in GIN structure. Moreover, a Full-Adjacent
(FA) layer is adopted for alleviating the over-squashing problem, which is
existed in all Graph Neural Network (GNN) structures, including GIN.
Furthermore, a multi-phase attention mechanism and multi-loss training strategy
are employed to avoid missing the useful emotional information in the stacked
WMA-GIN layers. We evaluated the performance of our proposed WMA-GIN on the
popular IEMOCAP dataset. The experimental results show that WMA-GIN outperforms
other GNN-based methods and is comparable to some advanced non-graph-based
methods by achieving 72.48% of weighted accuracy (WA) and 67.72% of unweighted
accuracy (UA).

本文提出一种基于图同构网络的语音情感识别（SER）网络，采用带权重的多聚合器（WMA-GIN）、全邻接层（FA）、多阶段注意机制和多损失训练策略，以解决 GIN 结构的邻居节点特征聚合容易导致的信息混淆、以及所有 GIN 等图神经网络结构的过度压缩问题，实现了比其它基于图神经网络和某些非图神经网络方法更好的结果，在 IEMOCAP 数据集中取得了 72.48% 和 67.72% 的加权和不加权识别精度。