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1. Current and future directions in network biology

   https://doi.org/10.1093/bioadv/vbae099  

   Zitnik, Marinka; Li, Michelle M; Wells, Aydin; Glass, Kimberly; Morselli_Gysi, Deisy; Krishnan, Arjun; Murali, T M; Radivojac, Predrag; Roy, Sushmita; Baudot, Anaïs; et al (January 2024, Bioinformatics Advances)

   Lengauer, Thomas (Ed.)

   Abstract SummaryNetwork biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology. Availability and implementationNot applicable. 

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2. Network properties determine neural network performance

   https://doi.org/10.1038/s41467-024-48069-8  

   Jiang, Chunheng; Huang, Zhenhan; Pedapati, Tejaswini; Chen, Pin-Yu; Sun, Yizhou; Gao, Jianxi (December 2024, Nature Communications)

   Abstract Machine learning influences numerous aspects of modern society, empowers new technologies, from Alphago to ChatGPT, and increasingly materializes in consumer products such as smartphones and self-driving cars. Despite the vital role and broad applications of artificial neural networks, we lack systematic approaches, such as network science, to understand their underlying mechanism. The difficulty is rooted in many possible model configurations, each with different hyper-parameters and weighted architectures determined by noisy data. We bridge the gap by developing a mathematical framework that maps the neural network’s performance to the network characters of the line graph governed by the edge dynamics of stochastic gradient descent differential equations. This framework enables us to derive a neural capacitance metric to universally capture a model’s generalization capability on a downstream task and predict model performance using only early training results. The numerical results on 17 pre-trained ImageNet models across five benchmark datasets and one NAS benchmark indicate that our neural capacitance metric is a powerful indicator for model selection based only on early training results and is more efficient than state-of-the-art methods. 

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3. Recommendations for sharing network data and materials

   https://doi.org/10.1017/nws.2024.16  

   Neal, Zachary P; Almquist, Zack W; Bagrow, James; Clauset, Aaron; Diesner, Jana; Lazega, Emmanuel; Lovato, Juniper; Moody, James; Peixoto, Tiago P; Steinert-Threlkeld, Zachary; et al (December 2024, Network Science)

   Abstract One of the goals of open science is to promote the transparency and accessibility of research. Sharing data and materials used in network research is critical to these goals. In this paper, we present recommendations for whether, what, when, and where network data and materials should be shared. We recommend that network data and materials should be shared, but access to or use of shared data and materials may be restricted if necessary to avoid harm or comply with regulations. Researchers should share the network data and materials necessary to reproduce reported results via a publicly accessible repository when an associated manuscript is published. To ensure the adoption of these recommendations, network journals should require sharing, and network associations and academic institutions should reward sharing. 

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4. Network Schema Preserving Heterogeneous Information Network Embedding

   https://doi.org/10.24963/ijcai.2020/190  

   Zhao, Jianan; Wang, Xiao; Shi, Chuan; Liu, Zekuan; Ye, Yanfang (July 2020, International Joint Conference on Artificial Intelligence (IJCAI))

   null (Ed.)

   As heterogeneous networks have become increasingly ubiquitous, Heterogeneous Information Network (HIN) embedding, aiming to project nodes into a low-dimensional space while preserving the heterogeneous structure, has drawn increasing attention in recent years. Many of the existing HIN embedding methods adopt meta-path guided random walk to retain both the semantics and structural correlations between different types of nodes. However, the selection of meta-paths is still an open problem, which either depends on domain knowledge or is learned from label information. As a uniform blueprint of HIN, the network schema comprehensively embraces the high-order structure and contains rich semantics. In this paper, we make the first attempt to study network schema preserving HIN embedding, and propose a novel model named NSHE. In NSHE, a network schema sampling method is first proposed to generate sub-graphs (i.e., schema instances), and then multi-task learning task is built to preserve the heterogeneous structure of each schema instance. Besides preserving pairwise structure information, NSHE is able to retain high-order structure (i.e., network schema). Extensive experiments on three real-world datasets demonstrate that our proposed model NSHE significantly outperforms the state-of-the-art methods. 

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5. Diverging Network Architecture of the C. elegans Connectome and Signaling Network

   https://doi.org/10.1103/6wgv-b9m6  

   Dvali, Sophie; Seguin, Caio; Betzel, Richard; Leifer, Andrew M (September 2025, PRX Life)

   The connectome describes the complete set of synaptic contacts through which neurons communicate. While the architecture of the connectome has been extensively characterized, much less is known about the organization of causal signaling networks arising from functional interactions between neurons. Understanding how effective communication pathways relate to or diverge from the underlying structure is a central question in neuroscience. Here we analyze the modular architecture of the signal propagation network, measured via calcium imaging and optogenetics, and compare it to the underlying anatomical wiring measured by electron microscopy. Compared to the connectome, we find that signaling modules are not aligned with the modular boundaries of the anatomical network, highlighting an instance where function deviates from structure. However, we find that some of the most striking features of the anatomical network are preserved, as exemplified by the pharynx, which is delineated into a separate community in both anatomy and signaling. We analyze the cellular compositions of the signaling architecture and find that its modules are enriched for specific cell types and functions, suggesting that the network modules are neurobiologically relevant. Lastly, we identify a “rich club” of hub neurons in the signaling network. The membership of the signaling rich club differs from the rich club detected in the anatomical network, challenging the view that structural hubs occupy positions of influence in functional (signaling) networks. The only overlap between the two rich clubs is given by neurons AVEL/R, which have some of the highest degrees in the anatomical network, again illustrating the preservation of the most pronounced features of the network. Our results provide new insight into the interplay between brain structure, in the form of a complete synaptic-level connectome, and brain function, in the form of a system-wide causal signal propagation atlas. 

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