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Found 12 papers, 6 papers with code
Global Counterfactual Explainer for Graph Neural Networks
One way to address this is counterfactual reasoning where the objective is to change the GNN prediction by minimal changes in the input graph.
Incorporating Heterophily into Graph Neural Networks for Graph Classification
Graph neural networks (GNNs) often assume strong homophily in graphs, seldom considering heterophily which means connected nodes tend to have different class labels and dissimilar features.
An Exploratory Study of Stock Price Movements from Earnings Calls
Third, the semantic features of transcripts are more predictive of stock price movements than sales and earnings per share, i. e., traditional hard data in most of the cases.
Task and Model Agnostic Adversarial Attack on Graph Neural Networks
In this work, we study this problem and show that GNNs remain vulnerable even when the downstream task and model are unknown.
A Neural Framework for Learning Subgraph and Graph Similarity Measures
Further, owing to its pair-independent embeddings and theoretical properties, NEUROSED allows approximately 3 orders of magnitude faster retrieval of graphs and subgraphs.
Event Detection on Dynamic Graphs
In this paper, we propose DyGED, a simple yet novel deep learning model for event detection on dynamic graphs.
NeuroSED: Learning Subgraph Similarity via Graph Neural Networks
Subgraph edit distance (SED) is one of the most expressive measures of subgraph similarity.
Feature-based Individual Fairness in k-Clustering
Ensuring fairness in machine learning algorithms is a challenging and important task.
Meta-Learning with Graph Neural Networks: Methods and Applications
Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems.
Investigating Ground-level Ozone Formation: A Case Study in Taiwan
In this paper, we aim to identify and understand the impact of various factors on O3 formation and predict the O3 concentrations under different pollution-reduced and climate change scenarios.
Manipulating Node Similarity Measures in Networks
These similarity measures turn out to be an important fundamental tool for many real world applications such as link prediction in networks, recommender systems etc.
Social and Information Networks Data Structures and Algorithms
Learning Heuristics over Large Graphs via Deep Reinforcement Learning
Additionally, a case-study on the practical combinatorial problem of Influence Maximization (IM) shows GCOMB is 150 times faster than the specialized IM algorithm IMM with similar quality.
