Network control theory (NCT) offers a robust analytical framework for
understanding the influence of network topology on dynamic behaviors, enabling
researchers to decipher how certain patterns of external control measures can
steer system dynamics towards desired states. Distinguished from other
structure-function methodologies, NCT's predictive capabilities can be coupled
with deploying Graph Neural Networks (GNNs), which have demonstrated
exceptional utility in various network-based learning tasks. However, the
performance of GNNs heavily relies on the expressiveness of node features, and
the lack of node features can greatly degrade their performance. Furthermore,
many real-world systems may lack node-level information, posing a challenge for
GNNs.To tackle this challenge, we introduce a novel approach, NCT-based
Enhanced Feature Augmentation (NCT-EFA), that assimilates average
controllability, along with other centrality indices, into the feature
augmentation pipeline to enhance GNNs performance. Our evaluation of NCT-EFA,
on six benchmark GNN models across two experimental setting. solely employing
average controllability and in combination with additional centrality metrics.
showcases an improved performance reaching as high as 11%. Our results
demonstrate that incorporating NCT into feature enrichment can substantively
extend the applicability and heighten the performance of GNNs in scenarios
where node-level information is unavailable.

在缺乏节点级信息的情况下，将网络控制理论（NCT）与特征增强相结合，可以大幅提高图神经网络（GNNs）的性能，进而拓展其应用范围。

基于网络控制理论的特征增强以提高图机器学习性能

Improving Graph Machine Learning Performance Through Feature  Augmentation Based on Network Control Theory

Recent advances in computational models of signal propagation and routing in
the human brain have underscored the critical role of white matter structure. A
complementary approach has utilized the framework of network control theory to
better understand how white matter constrains the manner in which a region or
set of regions can direct or control the activity of other regions. Despite the
potential for both of these approaches to enhance our understanding of the role
of network structure in brain function, little work has sought to understand
the relations between them. Here, we seek to explicitly bridge computational
models of communication and principles of network control in a conceptual
review of the current literature. By drawing comparisons between communication
and control models in terms of the level of abstraction, the dynamical
complexity, the dependence on network attributes, and the interplay of multiple
spatiotemporal scales, we highlight the convergence of and distinctions between
the two frameworks. Based on the understanding of the intertwined nature of
communication and control in human brain networks, this work provides an
integrative perspective for the field and outlines exciting directions for
future work.

探讨了网络控制理论和计算模型对理解人类大脑的作用方面的潜力，并通过比较两种框架从多个角度提供了有关这两种方法之间关系的信息。

脑网络的通信和控制模型：区别、汇聚和未来展望

Models of communication and control for brain networks: distinctions,  convergence, and future outlook

Cognitive function is driven by dynamic interactions between large-scale
neural circuits or networks, enabling behavior. Fundamental principles
constraining these dynamic network processes have remained elusive. Here we use
network control theory to offer a mechanistic explanation for how the brain
moves between cognitive states drawn from the network organization of white
matter microstructure. Our results suggest that densely connected areas,
particularly in the default mode system, facilitate the movement of the brain
to many easily-reachable states. Weakly connected areas, particularly in
cognitive control systems, facilitate the movement of the brain to
difficult-to-reach states. Areas located on the boundary between network
communities, particularly in attentional control systems, facilitate the
integration or segregation of diverse cognitive systems. Our results suggest
that structural network differences between the cognitive circuits dictate
their distinct roles in controlling dynamic trajectories of brain network
function.

运用网络控制理论，本文提供了一个关于大脑如何在不同认知状态之间转换的机制性解释，研究发现大脑的结构网络差异决定了其在控制网络功能动态轨迹中的不同作用。