Abstract: In recent years, Graph Neural Networks (GNNs) have achieved significant success in graph-based tasks. However, they still face challenges in complex scenarios, particularly in integrating ...

In this study, a novel self-cumulative contrastive graph clustering (SC-CGC) method is devised, which is capable of dynamically adjusting the influence of neighbors with different hops. Our intuition ...

The same logic should follow in that the largest value should start in the upper left corner, with other values following a descending order, clockwise, around the chart. Bar graphs make it easy to ...

Researchers at Harvard University and other institutes have introduced a new approach to control the interactions between high-frequency phonons and single solid-state quantum systems. Their ...

Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States Department of Chemistry and Biochemistry, University of California San Diego, La ...

Climate change has a substantial influence on the end of the growing season (EOS). The time-lag and cumulative effects are non-negligible phenomena when studying the interactions between climate and ...

Initially, time-frequency complexity and temporal features of the EEG signal are extracted to generate node features for a graph convolutional network. Subsequently, leveraging channel correlation, ...