no code implementations • 17 Apr 2024 • Chaoxi Niu, Guansong Pang, Ling Chen
Graph continual learning (GCL) tackles this problem by continually adapting GNNs to the expanded graph of the current task while maintaining the performance over the graph of previous tasks.
1 code implementation • 3 Jul 2023 • Chaoxi Niu, Guansong Pang, Ling Chen
One primary challenge is to learn normal patterns manifested in both fine-grained and holistic views of graphs for identifying graphs that are abnormal in part or in whole.
1 code implementation • 31 Jan 2023 • Chaoxi Niu, Guansong Pang, Ling Chen
To tackle this problem, this article proposes a novel approach that builds a discriminative model on collective affinity information (i. e., two sets of pairwise affinities between the negative instances and the anchor instance) to mine hard negatives in GCL.
1 code implementation • 2 Sep 2020 • Kun Zhan, Chaoxi Niu
We propose a new training method named as mutual teaching, i. e., we train dual models and let them teach each other during each batch.
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1 code implementation • 19 Jun 2019 • Changlu Chen, Chaoxi Niu, Xia Zhan, Kun Zhan
Based on the pretrained model and the constructed graph, we add a self-expressive layer to complete the generative model and then fine-tune it with a new loss function, including the reconstruction loss and a deliberately defined locality-preserving loss.