Abstract Massive deployment of Graph Neural Networks (GNNs) in high-stake applications
generates a strong demand for explanations that are robust to noise and align well with …

Maximizing on k-submodular functions subject to size constraint has received extensive
attention recently. In this paper, we investigate a more realistic scenario of this problem that …

Due to the growing importance of using graph neural networks in high-stakes applications,
there is a pressing need to interpret the predicted results of these models. Existing methods …

Interpreting the decision logic behind effective deep convolutional neural networks (CNN)
on images complements the success of deep learning models. However, the existing …

Given a social network G, a cost associated with each user, and an influence threshold η,
the minimum cost seed selection problem (MCSS) aims to find a set of seeds that minimizes …

In this paper, we consider the optimization problem Submodular Cover (SC), which is to find
a minimum cost subset of a ground set $ U $ such that the value of a submodular function $ f …

We consider the problem of maximizing submodular functions under submodular constraints
by formulating the problem in two ways: SCSKC and DiffC. Given two submodular functions f …

In this paper, we consider the optimization problem Submodular Cover (SCP), which is to
find a minimum cardinality subset of a finite universe $ U $ such that the value of a …

We introduce the problem of optimal congestion control in cache networks, whereby both
rate allocations and content placements are optimized jointly. We formulate this as a …

We study a cache network in which intermediate nodes equipped with caches can serve
requests. We model the problem of jointly optimizing caching and routing decisions with link …