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Found 13 papers, 7 papers with code
Towards Fair Graph Federated Learning via Incentive Mechanisms
Extensive experiments show that our model achieves the best trade-off between accuracy and the fairness of model gradient, as well as superior payoff fairness.
Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networks
The proposed pre-training pipeline is called the data-active graph pre-training (APT) framework, and is composed of a graph selector and a pre-training model.
DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning
We propose Multiple Experts Fine-tuning Framework to build a financial large language model (LLM), DISC-FinLLM.
Cross-Lingual Knowledge Editing in Large Language Models
With the recent advancements in large language models (LLMs), knowledge editing has been shown as a promising technique to adapt LLMs to new knowledge without retraining from scratch.
When to Pre-Train Graph Neural Networks? From Data Generation Perspective!
All convex combinations of graphon bases give rise to a generator space, from which graphs generated form the solution space for those downstream data that can benefit from pre-training.
Unifying Structure Reasoning and Language Model Pre-training for Complex Reasoning
Recent pre-trained language models (PLMs) equipped with foundation reasoning skills have shown remarkable performance on downstream complex tasks.
Understanding Translationese in Cross-Lingual Summarization
In detail, we find that (1) the translationese in documents or summaries of test sets might lead to the discrepancy between human judgment and automatic evaluation; (2) the translationese in training sets would harm model performance in real-world applications; (3) though machine-translated documents involve translationese, they are very useful for building CLS systems on low-resource languages under specific training strategies.
DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection
Since GAD emphasizes the application and the rarity of anomalous samples, enriching the varieties of its datasets is fundamental work.
A Unified Continuous Learning Framework for Multi-modal Knowledge Discovery and Pre-training
These two steps are iteratively performed in our framework for continuous learning.
DropMessage: Unifying Random Dropping for Graph Neural Networks
First, it is challenging to find a universal method that are suitable for all cases considering the divergence of different datasets and models.
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning
To bridge this gap, we present the Graph Robustness Benchmark (GRB) with the goal of providing a scalable, unified, modular, and reproducible evaluation for the adversarial robustness of GML models.
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs
To bridge the gap between theoretical graph attacks and real-world scenarios, in this work, we propose a novel and more realistic setting: strict black-box graph attack, in which the attacker has no knowledge about the victim model at all and is not allowed to send any queries.
Unsupervised Adversarially-Robust Representation Learning on Graphs
Additionally, we explore a provable connection between the robustness of the unsupervised graph encoder and that of models on downstream tasks.
