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Found 8 papers, 4 papers with code
A Fast Maximum Clique Algorithm Based on Network Decomposition for Large Sparse Networks
A bunch of effective techniques is being used to greatly prune the graph and a novel concept called Complete-Upper-Bound-Induced Subgraph (CUBIS) is proposed to ensure that the structures with the potential to form the maximum clique are retained in the process of graph decomposition.
A Quick Framework for Evaluating Worst Robustness of Complex Networks
Robustness is pivotal for comprehending, designing, optimizing, and rehabilitating networks, with simulation attacks being the prevailing evaluation method.
Comprehensive Analysis of Network Robustness Evaluation Based on Convolutional Neural Networks with Spatial Pyramid Pooling
However, the performance of the proposed CNN model varies: for evaluation tasks that are consistent with the trained network type, the proposed CNN model consistently achieves accurate evaluations of both attack curves and robustness values across all removal scenarios.
LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction
As deep learning technology advances and more urban spatial-temporal data accumulates, an increasing number of deep learning models are being proposed to solve urban spatial-temporal prediction problems.
Continuous Trajectory Generation Based on Two-Stage GAN
Simulating the human mobility and generating large-scale trajectories are of great use in many real-world applications, such as urban planning, epidemic spreading analysis, and geographic privacy protect.
LibCity: An Open Library for Traffic Prediction
This paper presents LibCity, a unified, comprehensive, and extensible library for traffic prediction, which provides researchers with a credible experimental tool and a convenient development framework.
Multivariate Time Series Forecasting
Spatio-Temporal Forecasting
+2
Efficient Sampling Algorithms for Approximate Temporal Motif Counting (Extended Version)
We first propose a generic edge sampling (ES) algorithm for estimating the number of instances of any temporal motif.
STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural Networks
IoT applications, however, often measure physical phenomena, where the underlying physics (such as inertia, wireless signal propagation, or the natural frequency of oscillation) are fundamentally a function of signal frequencies, offering better features in the frequency domain.
