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Machine unlearning seeks to remove the influence of designated training data from a trained model without retraining from scratch. Retrain-free methods such as Selective Synaptic Dampening (SSD) and its label-free variant LFSSD avoid full retraining but still require backpropagation and parameter importance computed ov...
Real-world social relationships are not uniformly supportive. Information through hostile connections can increase resistance, anxiety, or misinformation rather than adoption. Classical models such as Independent Cascade and Linear Threshold, together with Influence Maximization (IM), which maximizes spread from a limi...
A critical and comparative survey of centrality measures in temporal networks is presented, and a functional taxonomy that categorizes existing approaches according to the primary mechanism through which influence is quantified in temporal networks is proposed.
Spectral Efficiency Centrality (SEC), a temporal spectral centrality framework that quantifies node importance by evaluating the change in spectral radius caused by node removal across temporal snapshots, and ASEC, an efficient approximation based on Perron-Frobenius theory and first-order eigenvalue perturbation that...
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