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Found 22 papers, 3 papers with code
Resource Constrained Pathfinding with A* and Negative Weights
Constrained pathfinding is a well-studied, yet challenging network optimisation problem that can be seen in a broad range of real-world applications.
Parallelizing Multi-objective A* Search
The Multi-objective Shortest Path (MOSP) problem is a classic network optimization problem that aims to find all Pareto-optimal paths between two points in a graph with multiple edge costs.
A Comprehensive Survey on Spectral Clustering with Graph Structure Learning
To bridge this gap, this survey presents a comprehensive review of spectral clustering methods, emphasizing on the critical role of GSL.
Resource Constrained Pathfinding with Enhanced Bidirectional A* Search
The classic Resource Constrained Shortest Path (RCSP) problem aims to find a cost optimal path between a pair of nodes in a network such that the resources used in the path are within a given limit.
Real-Time Energy-Optimal Path Planning for Electric Vehicles
The rapid adoption of electric vehicles (EVs) in modern transport systems has made energy-aware routing a critical task in their successful integration, especially within large-scale networks.
Online Electric Vehicle Charging Detection Based on Memory-based Transformer using Smart Meter Data
It dynamically leverages coarse-scale historical information using an M-TR encoder from an extended global temporal window, in conjunction with an M-TR decoder that concentrates on a limited time frame, local window, aiming to capture the fine-scale characteristics of the smart meter data.
Identifying the Most Influential Driver Nodes for Pinning Control of Multi-Agent Systems with Time-Varying Topology
Identifying the most influential driver nodes to guarantee the fastest synchronization speed is a key topic in pinning control of multi-agent systems.
Clustering-based Multitasking Deep Neural Network for Solar Photovoltaics Power Generation Prediction
For each type, a deep neural network (DNN) is employed and trained until the accuracy cannot be improved.
Neural Graph Collaborative Filtering Using Variational Inference
The customization of recommended content to users holds significant importance in enhancing user experiences across a wide spectrum of applications such as e-commerce, music, and shopping.
Electric Vehicle E-hailing Fleet Dispatching and Charge Scheduling
With recent developments in vehicle and battery technologies, electric vehicles (EVs) are rapidly getting established as a sustainable alternative to traditional fossil-fuel vehicles.
Enhanced Methods for the Weight Constrained Shortest Path Problem
This paper leverages the recent state-of-the-art techniques in both constrained pathfinding and bi-objective search and presents two new solution approaches to the WCSPP on the basis of A* search, both capable of solving hard WCSPP instances on very large graphs.
Vital Node Identification in Complex Networks Using a Machine Learning-Based Approach
Vital node identification is the problem of finding nodes of highest importance in complex networks.
Learning Asymmetric Embedding for Attributed Networks via Convolutional Neural Network
The final representations are the results of concatenating source and target embedding vectors.
Transfer Learning for Multi-lingual Tasks -- a Survey
These days different platforms such as social media provide their clients from different backgrounds and languages the possibility to connect and exchange information.
Multi-objective Scheduling of Electric Vehicle Charging/Discharging with Time of Use Tariff
Optimal coordinated charging is a multi-objective optimization problem (MOOP) in nature, with objective functions such as minimum price charging and minimum disruptions to the grid.
Who will accept my request? Predicting response of link initiation in two-way relation networks
We propose a methodology to solve the link initiation feedback prediction problem in this multilayer fashion.
Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks
We believe we are the first to use embeddings for the task of fair influence maximization.
Detection of Community Structures in Networks with Nodal Features based on Generative Probabilistic Approach
The proposed approach learns the relevant features of communities through a generative probabilistic model without any prior assumption on the communities.
Self-Paced Multi-Label Learning with Diversity
The major challenge of learning from multi-label data has arisen from the overwhelming size of label space which makes this problem NP-hard.
Link Prediction in Multiplex Networks based on Interlayer Similarity
Multilayer networks with similar nodes across layers are also known as multiplex networks.
Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting
The smart metering infrastructure has changed how electricity is measured in both residential and industrial application.
Australia's long-term electricity demand forecasting using deep neural networks
Accurate prediction of long-term electricity demand has a significant role in demand side management and electricity network planning and operation.
