Graph Neural Networks (GNN) offer the powerful approach to node classification in complex networks across many domains including social media, E-commerce, and FinTech. However, recent studies show that GNNs are vulnerable to attacks aimed at adversely impacting their node classification performance. Existing studies of adversarial attacks on GNN focus primarily on manipulating the connectivity between existing nodes, a task that requires greater effort on the part of the attacker in real-world applications. In contrast, it is much more expedient on the part of the attacker to inject adversarial nodes, e.g., fake profiles with forged links, into existing graphs so asmore »
This content will become publicly available on April 1, 2023
Batch Active Learning with Graph Neural Networks via Multi-Agent Deep Reinforcement Learning
Graph neural networks (GNNs) have achieved tremendous success in many graph learning tasks such as node classifica- tion, graph classification and link prediction. For the classifi- cation task, GNNs’ performance often highly depends on the number of labeled nodes and thus could be significantly ham- pered due to the expensive annotation cost. The sparse litera- ture on active learning for GNNs has primarily focused on se- lecting only one sample each iteration, which becomes ineffi- cient for large scale datasets. In this paper, we study the batch active learning setting for GNNs where the learning agent can acquire labels of multiple samples at each time. We formu- late batch active learning as a cooperative multi-agent rein- forcement learning problem and present a novel reinforced batch-mode active learning framework (BIGENE). To avoid the combinatorial explosion of the joint action space, we in- troduce a value decomposition method that factorizes the to- tal Q-value into the average of individual Q-values. More- over, we propose a novel multi-agent Q-network consisting of a graph convolutional network (GCN) component and a gated recurrent unit (GRU) component. The GCN compo- nent takes both the informativeness and inter-dependences between nodes into account and the GRU component enables more »
- Publication Date:
- NSF-PAR ID:
- 10332501
- Journal Name:
- AAAI 2022
- Sponsoring Org:
- National Science Foundation
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