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Found 23 papers, 10 papers with code
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.
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
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.
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.
Feature-based Individual Fairness in k-Clustering
Ensuring fairness in machine learning algorithms is a challenging and essential task.
NeuroSED: Learning Subgraph Similarity via Graph Neural Networks
Subgraph edit distance (SED) is one of the most expressive measures of subgraph similarity.
Event Detection on Dynamic Graphs
In this paper, we propose DyGED, a simple yet novel deep learning model for event detection on dynamic graphs.
GREED: A Neural Framework for Learning Graph Distance Functions
To elaborate, although GED is a metric, its neural approximations do not provide such a guarantee.
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.
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.
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.
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.
Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis
Often, deep network models are purely inductive during training and while performing inference on unseen data.
Ranked #7 on
Visual Question Answering
on VQA v2 test-dev
A Survey on Explainability of Graph Neural Networks
Graph neural networks (GNNs) are powerful graph-based deep-learning models that have gained significant attention and demonstrated remarkable performance in various domains, including natural language processing, drug discovery, and recommendation systems.
Empowering Counterfactual Reasoning over Graph Neural Networks through Inductivity
Graph neural networks (GNNs) have various practical applications, such as drug discovery, recommendation engines, and chip design.
GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking
Motivated by this need, we present a benchmarking study on perturbation-based explainability methods for GNNs, aiming to systematically evaluate and compare a wide range of explainability techniques.
NeuroCUT: A Neural Approach for Robust Graph Partitioning
Second, we decouple the parameter space and the partition count making NeuroCUT inductive to any unseen number of partition, which is provided at query time.
COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial Problems
Combinatorial Optimization (CO) problems over graphs appear routinely in many applications such as in optimizing traffic, viral marketing in social networks, and matching for job allocation.
DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization
Graph clustering is a fundamental and challenging task in the field of graph mining where the objective is to group the nodes into clusters taking into consideration the topology of the graph.
VeriBug: An Attention-based Framework for Bug-Localization in Hardware Designs
Then, it assigns an importance score to each operand in a design statement and uses that score for generating explanations for failures.
Game-theoretic Counterfactual Explanation for Graph Neural Networks
Our results reveals that computing Banzhaf values requires lower sample complexity in identifying the counterfactual explanations compared to other popular methods such as computing Shapley values.
Uncertainty in Graph Neural Networks: A Survey
Therefore, identifying, quantifying, and utilizing uncertainty are essential to enhance the performance of the model for the downstream tasks as well as the reliability of the GNN predictions.
A Comprehensive Survey on AI-based Methods for Patents
This progress extends to the field of patent analysis and innovation, where AI-based tools present opportunities to streamline and enhance important tasks in the patent cycle such as classification, retrieval, and valuation prediction.
