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Related Articles from SNS
Learn When and Where to Connect: Adaptive Virtual Nodes for Dynamic Message Passing on Graphs
arXiv:2606.03068v1 Announce Type: new Abstract: While Virtual Nodes (VNs) are often utilized in Message Passing Neural Networks (MPNNs) to facilitate effective message passing, existing VN-based methods have limitations, such as constraining all nodes to connect to the same number of VNs, fixing the connections before applying MPNNs, and connecting a node to a VN independently of the other nodes that connect to the same VN. We propose MAVN, an end-to-end differentiable MPNN framework that...
ALINC: Active Learning for Inductive Node Classification via Graph Sampling
Announce Type: new Abstract: Active learning (AL) for node classification typically focuses on selecting the most informative nodes for annotation within one or a few large graphs (e.g., in social network analysis). However, in other domains, such as molecular chemistry or electronic design automation, datasets consist of thousands of independent graphs. In many of these inductive settings, annotating an individual node requires a full-graph analysis, which effectively yields the remaining...
Network node immunization: improving Netshield algorithm through random rooted forests
arXiv:2606.04131v1 Announce Type: new Abstract: We are interested in the so-called multiple-node immunization problem for complex networks under attack by a viral agent. It consists in identifying and removing a set of nodes of size $k$ in a graph to maximize the impeding of virus spread. A few approaches have been proposed in the literature based on numerical and theoretical insights on how classical models for virus spread evolve on graphs.
A Lightweight Deep Learning-based Model for Ranking Influential Nodes in Complex Networks
Announce Type: cross Abstract: Identifying influential nodes in complex networks is a critical task with a wide range of applications across different domains. However, existing approaches often face trade-offs between accuracy and computational efficiency. To address these challenges, we propose 1D-CGS, a lightweight and effective hybrid model that integrates the speed of one-dimensional convolutional neural networks (1D-CNN) with the topological representation power of GraphSAGE for...
Trajectory-Aware Node Contributions and the Limits of Static Controllability
Announce Type: replace-cross Abstract: A recurring data mining task in complex networks is to determine how individual nodes contribute to system behavior. Existing approaches rely on either static-graph centralities or control-theoretic quantities such as controllability Gramians, which assume linear, time-invariant dynamics. Estimated systems, however, are typically nonlinear and time-varying.
Trajectory-Aware Node Contributions and the Limits of Static Controllability
arXiv:2606.03067v1 Announce Type: cross Abstract: A recurring data mining task in complex networks is to determine how individual nodes contribute to system behavior. Existing approaches rely on either static-graph centralities or control-theoretic quantities such as controllability Gramians, which assume linear, time-invariant dynamics. Estimated systems, however, are typically nonlinear and time-varying.
Intel bit off more than it could chew with 18A process node
Intel is keen to reassure investors that its troubles with the 18A manufacturing process were a one-off, and that it is better positioned to capitalize on what it expects will be growing demand for CPUs used in AI inference workloads. Speaking at the Bank of America 2026 Global Technology Conference in San Francisco, Chipzilla’s chief financial officer David Zinsner claimed that the firm simply bit off more than it could chew in trying to move too fast with the new process node. “I would say...
Adaptive Node Feature Selection For Graph Neural Networks
arXiv:2510.03096v3 Announce Type: replace Abstract: We propose an adaptive node feature selection approach for graph neural networks (GNNs) that identifies and removes unnecessary features during training. The ability to measure how features contribute to model output is key for interpreting decisions and reducing dimensionality by eliminating unhelpful variables. However, graph-structured data introduces complex dependencies that may be unsuited to classical feature importance metrics.
Deployed trusted-node quantum key distribution over 300 km with a multi-core fiber access link
Announce Type: cross Abstract: Quantum key distribution (QKD) is increasingly considered for deployment in realistic communication networks, where long distances, heterogeneous fiber infrastructure, and coexistence with classical traffic present substantial challenges. Here, we demonstrate trusted-node QKD between Link\"oping University and the Stockholm hub of the Swedish national quantum communication infrastructure over 270 km of deployed single-mode fiber, extended by a 33 km multi-core...
Deployed trusted-node quantum key distribution over 300 km with a multi-core fiber access link
Announce Type: cross Abstract: Quantum key distribution (QKD) is increasingly considered for deployment in realistic communication networks, where long distances, heterogeneous fiber infrastructure, and coexistence with classical traffic present substantial challenges. Here, we demonstrate trusted-node QKD between Link\"oping University and the Stockholm hub of the Swedish national quantum communication infrastructure over 270 km of deployed single-mode fiber, extended by a 33 km multi-core...