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Found 10 papers, 7 papers with code
Cooperative Network Learning for Large-Scale and Decentralized Graphs
Here, we introduce a Cooperative Network Learning (CNL) framework to ensure secure graph computing for various graph tasks.
Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes
Despite the recent successes of vanilla Graph Neural Networks (GNNs) on various tasks, their foundation on pairwise networks inherently limits their capacity to discern latent higher-order interactions in complex systems.
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Influential Simplices Mining via Simplicial Convolutional Network
It can tackle higher-order tasks by leveraging novel higher-order presentations: hierarchical bipartite graphs and higher-order hierarchical (HoH) Laplacians, where targeted simplices are grouped into a hub set and can interact with other simplices.
Complete cavity map of the C. elegans connectome
This study reports for the first time a complete cavity map of C. elegans neural network, developing a new method for mining higher-order structures that can be applied by researchers in neuroscience, network science and other interdisciplinary fields to explore higher-order structural markers of complex systems.
DynamicLight: Dynamically Tuning Traffic Signal Duration with DRL
Deep reinforcement learning (DRL) is becoming increasingly popular in implementing traffic signal control (TSC).
Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control
Many studies confirmed that a proper traffic state representation is more important than complex algorithms for the classical traffic signal control (TSC) problem.
Efficient Pressure: Improving efficiency for signalized intersections
Since conventional approaches could not adapt to dynamic traffic conditions, reinforcement learning (RL) has attracted more attention to help solve the traffic signal control (TSC) problem.
Hyperparameter-free and Explainable Whole Graph Embedding
Graphs can be used to describe complex systems.
Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data
Despite the increasing use of citation-based metrics for research evaluation purposes, we do not know yet which metrics best deliver on their promise to gauge the significance of a scientific paper or a patent.
Recommender Systems
In this article, we review recent developments in recommender systems and discuss the major challenges.
