Message-Passing Neural Networks (MPNNs) are extensively employed in graph
learning tasks but suffer from limitations such as the restricted scope of
information exchange, by being confined to neighboring nodes during each round
of message passing. Various strategies have been proposed to address these
limitations, including incorporating virtual nodes to facilitate global
information exchange. In this study, we introduce the Hierarchical Support
Graph (HSG), an extension of the virtual node concept created through recursive
coarsening of the original graph. This approach provides a flexible framework
for enhancing information flow in graphs, independent of the specific MPNN
layers utilized. We present a theoretical analysis of HSGs, investigate their
empirical performance, and demonstrate that HSGs can surpass other methods
augmented with virtual nodes, achieving state-of-the-art results across
multiple datasets.

通过引入分层支持图 (HSG)，该研究在 MPNN 中提出了一种解决局限性的策略，以促进图中的全局信息交流，经过理论分析和实证研究发现，HSG 能够在多个数据集上达到最先进的性能。

使用分级支持图进行下一级的消息传递

Next Level Message-Passing with Hierarchical Support Graphs

Decentralized learning (DL) enables collaborative learning without a server
and without training data leaving the users' devices. However, the models
shared in DL can still be used to infer training data. Conventional privacy
defenses such as differential privacy and secure aggregation fall short in
effectively safeguarding user privacy in DL. We introduce Shatter, a novel DL
approach in which nodes create virtual nodes (VNs) to disseminate chunks of
their full model on their behalf. This enhances privacy by (i) preventing
attackers from collecting full models from other nodes, and (ii) hiding the
identity of the original node that produced a given model chunk. We
theoretically prove the convergence of Shatter and provide a formal analysis
demonstrating how Shatter reduces the efficacy of attacks compared to when
exchanging full models between participating nodes. We evaluate the convergence
and attack resilience of Shatter with existing DL algorithms, with
heterogeneous datasets, and against three standard privacy attacks, including
gradient inversion. Our evaluation shows that Shatter not only renders these
privacy attacks infeasible when each node operates 16 VNs but also exhibits a
positive impact on model convergence compared to standard DL. This enhanced
privacy comes with a manageable increase in communication volume.

Shatter 是一种新型的分散式学习方法，通过创建虚拟节点（VNs）来增强隐私保护，防止攻击者收集完整模型并隐藏原始节点的身份。该方法在提高隐私安全性的同时，对于模型收敛性也有积极影响。

超越噪声：带有虚拟节点的隐私保护去中心化学习

Beyond Noise: Privacy-Preserving Decentralized Learning with Virtual  Nodes

Being able to identify regions within or around proteins, to which ligands
can potentially bind, is an essential step to develop new drugs. Binding site
identification methods can now profit from the availability of large amounts of
3D structures in protein structure databases or from AlphaFold predictions.
Current binding site identification methods heavily rely on graph neural
networks (GNNs), usually designed to output E(3)-equivariant predictions. Such
methods turned out to be very beneficial for physics-related tasks like binding
energy or motion trajectory prediction. However, the performance of GNNs at
binding site identification is still limited potentially due to the lack of
dedicated nodes that model hidden geometric entities, such as binding pockets.
In this work, we extend E(n)-Equivariant Graph Neural Networks (EGNNs) by
adding virtual nodes and applying an extended message passing scheme. The
virtual nodes in these graphs are dedicated quantities to learn representations
of binding sites, which leads to improved predictive performance. In our
experiments, we show that our proposed method VN-EGNN sets a new
state-of-the-art at locating binding site centers on COACH420, HOLO4K and
PDBbind2020.

通过添加虚拟节点和应用扩展的信息传递方案，扩展了 E (n)-Equivariant Graph Neural Networks (EGNNs)，以提高预测性能，并在 COACH420、HOLO4K 和 PDBbind2020 上表现出卓越的定位结合位点中心的最新技术。