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Found 9 papers, 3 papers with code
Transform then Explore: a Simple and Effective Technique for Exploratory Combinatorial Optimization with Reinforcement Learning
The technique is originated from physics, but is very effective in enabling RL agents to explore to continuously improve the solutions during test.
Machine Learning for the Multi-Dimensional Bin Packing Problem: Literature Review and Empirical Evaluation
To the best of our knowledge, this is the first systematic review of ML-related methods for BPP.
Finding Influencers in Complex Networks: An Effective Deep Reinforcement Learning Approach
Maximizing influences in complex networks is a practically important but computationally challenging task for social network analysis, due to its NP- hard nature.
The Expressive Power of Graph Neural Networks: A Survey
Graph neural networks (GNNs) are effective machine learning models for many graph-related applications.
Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks
Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges.
Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks
Since such temporal knowledge graphs often suffer from incompleteness, it is important to develop time-aware representation learning models that help to infer the missing temporal facts.
Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer
Predicting missing facts in a knowledge graph (KG) is a crucial task in knowledge base construction and reasoning, and it has been the subject of much research in recent works using KG embeddings.
Ranked #2 on
Knowledge Graph Completion
on DPB-5L (French)
Pre-Training Graph Neural Networks for Generic Structural Feature Extraction
With the proposed pre-training procedure, the generic structural information is learned and preserved, thus the pre-trained GNN requires less amount of labeled data and fewer domain-specific features to achieve high performance on different downstream tasks.
Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach
By training on small-scale networks, the learned model is capable of assigning relative BC scores to nodes for any unseen networks, and thus identifying the highly-ranked nodes.
