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Found 11 papers, 5 papers with code
Online Influence Maximization: Concept and Algorithm
Here, we summarize three types of feedback in the CMAB model and discuss in detail how to study the Online IM problem based on the CMAB-T model.
DSCom: A Data-Driven Self-Adaptive Community-Based Framework for Influence Maximization in Social Networks
Several previous works have addressed this topic in a statistical way and provided efficient algorithms with theoretical guarantee.
Adversarial Bandits with Multi-User Delayed Feedback: Theory and Application
Thus, we formulate an adversarial MAB problem with multi-user delayed feedback and design a modified EXP3 algorithm MUD-EXP3, which makes a decision at each round by considering the importance-weighted estimator of the received feedback from different users.
A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing System
Terahertz communication networks and intelligent reflecting surfaces exhibit significant potential in advancing wireless networks, particularly within the domain of aerial-based multi-access edge computing systems.
FedCL: Federated Multi-Phase Curriculum Learning to Synchronously Correlate User Heterogeneity
However, a significant challenge in FL is handling the heterogeneity of local data distribution, which often results in a drifted global model that is difficult to converge.
A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization
Therefore, we abandon the traditional algorithms based on iterative search and review the recent development of ML-based methods, especially Deep Reinforcement Learning, to solve the IM problem and other variants in social networks.
ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement Learning
Aiming at selecting a small subset of nodes with maximum influence on networks, the Influence Maximization (IM) problem has been extensively studied.
Look Closer to Your Enemy: Learning to Attack via Teacher-Student Mimicking
Deep neural networks have significantly advanced person re-identification (ReID) applications in the realm of the industrial internet, yet they remain vulnerable.
Graph Representation Learning for Popularity Prediction Problem: A Survey
In this paper, we present a comprehensive review for existing works using GRL methods for popularity prediction problem, and categorize related literatures into two big classes, according to their mainly used model and techniques: embedding-based methods and deep learning methods.
A Double Auction for Charging Scheduling among Vehicles Using DAG-Blockchains
In this process, a constrained multi-item double auction problem is formulated because of the limited charging resources in a CS, which motivates EVs and CSs in this area to participate in the market based on their preferences and statuses.
Networking and Internet Architecture Computer Science and Game Theory
Continuous Profit Maximization: A Study of Unconstrained Dr-submodular Maximization
The objective function of our CPM-MS is dr-submodular, but non-monotone.
