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Found 14 papers, 3 papers with code
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.
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.
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.
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.
Link Prediction in Multiplex Networks based on Interlayer Similarity
Multilayer networks with similar nodes across layers are also known as multiplex networks.
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.
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.
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.
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.
Learning Asymmetric Embedding for Attributed Networks via Convolutional Neural Network
The final representations are the results of concatenating source and target embedding vectors.
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.
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.
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.
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.
