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.
Then, it assigns an importance score to each operand in a design statement and uses that score for generating explanations for failures.
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.
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.
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.
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.
Graph neural networks (GNNs) have various practical applications, such as drug discovery, recommendation engines, and chip design.
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.
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
One way to address this is counterfactual reasoning where the objective is to change the GNN prediction by minimal changes in the input graph.
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.
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.
In this work, we study this problem and show that GNNs remain vulnerable even when the downstream task and model are unknown.
To elaborate, although GED is a metric, its neural approximations do not provide such a guarantee.
Subgraph edit distance (SED) is one of the most expressive measures of subgraph similarity.
Ensuring fairness in machine learning algorithms is a challenging and essential task.
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.
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.
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
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.