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Found 6 papers, 6 papers with code
Influence Maximization in Hypergraphs using Multi-Objective Evolutionary Algorithms
Among the various methods for solving the IM problem, evolutionary algorithms (EAs) have been shown to be particularly effective.
MULTIFLOW: Shifting Towards Task-Agnostic Vision-Language Pruning
In this challenging setting, the transferable representations already encoded in the pretrained model are a key aspect to preserve.
Many-Objective Evolutionary Influence Maximization: Balancing Spread, Budget, Fairness, and Time
The Influence Maximization (IM) problem seeks to discover the set of nodes in a graph that can spread the information propagation at most.
Neuron-centric Hebbian Learning
To overcome this limitation, we propose a novel plasticity model, called Neuron-centric Hebbian Learning (NcHL), where optimization focuses on neuron- rather than synaptic-specific Hebbian parameters.
Understanding Sparse Neural Networks from their Topology via Multipartite Graph Representations
With these novelties, we show the following: (a) The proposed MGE allows to extract topological metrics that are much better predictors of the accuracy drop than metrics computed from current input-agnostic BGEs; (b) Which metrics are important at different sparsity levels and for different architectures; (c) A mixture of our topological metrics can rank PaI algorithms more effectively than Ramanujan-based metrics.
Large-scale multi-objective influence maximisation with network downscaling
Finding the most influential nodes in a network is a computationally hard problem with several possible applications in various kinds of network-based problems.
