Public articles linked to the same research event.
arXiv The work proposes ChronoSpike, an adaptive spiking graph neural network that integrates learnable LIF neurons with per-channel membrane dynamics, multi-head spatially-attentive aggregation over continuous features, and a lightweight Transformer temporal encoder; it outperforms twelve state-of-the-art baselines on three large benchmarks by 2.0% Macro-F1 and 2.4% Micro-F1 on average, trains 3-10 times faster than recurrent methods, holds a constant 105K-parameter budget independent of graph size, provides theoretical guarantees for membrane potential boundedness, gradient flow stability under contraction factor ρ<1, and BIBO stability, and its interpretability analyses reveal heterogeneous temporal receptive fields and a learned primacy effect with 83-88% sparsity.
The work proposes ChronoSpike, an adaptive spiking graph neural network that integrates learnable LIF neurons with per-channel membrane dynamics, multi-head spatially-attentive aggregation over continuous features, and a lightweight Transformer temporal encoder; it outperforms twelve state-of-the-art baselines on three large benchmarks by 2.0% Macro-F1 and 2.4% Micro-F1 on average, trains 3-10 times faster than recurrent methods, holds a constant 105K-parameter budget independent of graph size, provides theoretical guarantees for membrane potential boundedness, gradient flow stability under contraction factor ρ<1, and BIBO stability, and its interpretability analyses reveal heterogeneous temporal receptive fields and a learned primacy effect with 83-88% sparsity.
The work proposes ChronoSpike, an adaptive spiking graph neural network that integrates learnable LIF neurons with per-channel membrane dynamics, multi-head spatially-attentive aggregation over continuous features, and a lightweight Transformer temporal encoder; it outperforms twelve state-of-the-art baselines on three large benchmarks by 2.0% Macro-F1 and 2.4% Micro-F1 on average, trains 3-10 times faster than recurrent methods, holds a constant 105K-parameter budget independent of graph size, provides theoretical guarantees for membrane potential boundedness, gradient flow stability under contraction factor ρ<1, and BIBO stability, and its interpretability analyses reveal heterogeneous temporal receptive fields and a learned primacy effect with 83-88% sparsity.
The work proposes ChronoSpike, an adaptive spiking graph neural network that integrates learnable LIF neurons with per-channel membrane dynamics, multi-head spatially-attentive aggregation over continuous features, and a lightweight Transformer temporal encoder; it outperforms twelve state-of-the-art baselines on three large benchmarks by 2.0% Macro-F1 and 2.4% Micro-F1 on average, trains 3-10 times faster than recurrent methods, holds a constant 105K-parameter budget independent of graph size, provides theoretical guarantees for membrane potential boundedness, gradient flow stability under contraction factor ρ<1, and BIBO stability, and its interpretability analyses reveal heterogeneous temporal receptive fields and a learned primacy effect with 83-88% sparsity.