53 citations · 100 across the 10 of their papers we have counts for
5 papers · 1 filter
AKBR: Learning Adaptive Kernel-based Representations for Graph Classification
Feifei Qian, Lixin Cui, Ming Li +6
In this paper, we propose a new model to learn Adaptive Kernel-based Representations (AKBR) for graph classification. Unlike state-of-the-art R-convolution graph kernels that are d…
Generative Temporal Link Prediction via Self-tokenized Sequence Modeling
Yue Wang, Chenwei Zhang, Shen Wang +4
We formalize networks with evolving structures as temporal networks and propose a generative link prediction model, Generative Link Sequence Modeling (GLSM), to predict future link…
Competitive Multi-Agent Deep Reinforcement Learning with Counterfactual Thinking
Yue Wang, Yao Wan, Chenwei Zhang +3
Counterfactual thinking describes a psychological phenomenon that people re-infer the possible results with different solutions about things that have already happened. It helps pe…
Identifying The Most Informative Features Using A Structurally Interacting Elastic Net
Lixin Cui, Lu Bai, Zhihong Zhang +2
Feature selection can efficiently identify the most informative features with respect to the target feature used in training. However, state-of-the-art vector-based methods are una…
Graph Convolutional Neural Networks based on Quantum Vertex Saliency
Lu Bai, Yuhang Jiao, Luca Rossi +3
This paper proposes a new Quantum Spatial Graph Convolutional Neural Network (QSGCNN) model that can directly learn a classification function for graphs of arbitrary sizes. Unlike…