Combinatorial Optimisation problems arise in several application domains and are often
formulated in terms of graphs. Many of these problems are NP-hard, but exact solutions are …

There has been an increased interest in discovering heuristics for combinatorial problems
on graphs through machine learning. While existing techniques have primarily focused on …

The design of good heuristics or approximation algorithms for NP-hard combinatorial
optimization problems often requires significant specialized knowledge and trial-and-error …

A serious challenge when finding influential actors in real-world social networks is the lack
of knowledge about the structure of the underlying network. Current state-of-the-art methods …

Influence maximization (IM) is formulated as selecting a set of initial users from a social
network to maximize the expected number of influenced users. Researchers have made …

Influence maximization is a widely studied topic in network science, where the aim is to
reach the maximum possible number of nodes, while only targeting a small initial set of …

There has been an increased interest in discovering heuristics for combinatorial problems
on graphs through machine learning. While existing techniques have primarily focused on …

Data selection methods, such as active learning and core-set selection, are useful tools for
improving the data efficiency of deep learning models on large-scale datasets. However …

The phenomena of influence diffusion on social networks have received tremendous
research interests in the past decade. While most prior works mainly focus on predicting the …

We consider the influence maximization problem (selecting $ k $ seeds in a network
maximizing the expected total influence) on undirected graphs under the linear threshold …