Papers › Dynamic Graph Information Bottleneck

Dynamic Graph Information Bottleneck

9 Feb 2024arXiv:2402.06716archive 2025-07-28

Haonan Yuan, Qingyun Sun, Xingcheng Fu, Cheng Ji, JianXin Li

Dynamic Graphs widely exist in the real world, which carry complicated spatial and temporal feature patterns, challenging their representation learning. Dynamic Graph Neural Networks (DGNNs) have shown impressive predictive abilities by exploiting the intrinsic dynamics. However, DGNNs exhibit limited robustness, prone to adversarial attacks. This paper presents the novel Dynamic Graph Information Bottleneck (DGIB) framework to learn robust and discriminative representations. Leveraged by the Information Bottleneck (IB) principle, we first propose the expected optimal representations should satisfy the Minimal-Sufficient-Consensual (MSC) Condition. To compress redundant as well as conserve meritorious information into latent representation, DGIB iteratively directs and refines the structural and feature information flow passing through graph snapshots. To meet the MSC Condition, we decompose the overall IB objectives into DGIB_(MS) and DGIB_C, in which the DGIB_(MS) channel aims to learn the minimal and sufficient representations, with the DGIB_(MS) channel guarantees the predictive consensus. Extensive experiments on real-world and synthetic dynamic graph datasets demonstrate the superior robustness of DGIB against adversarial attacks compared with state-of-the-art baselines in the link prediction task. To the best of our knowledge, DGIB is the first work to learn robust representations of dynamic graphs grounded in the information-theoretic IB principle.

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get_reparam_num_neurons ringbdstack/dgib/DGIB/util_IB.py official repository ran MIT (permissive) · 01e22d114b1da6d8 · report
recursive_index ringbdstack/dgib/DGIB/pytorch_net/util.py official repository ran MIT (permissive) · 9e275404aa5e3c6a · report
sample_lognormal ringbdstack/dgib/DGIB/util_IB.py official repository ran fingerprinted MIT (permissive) · 62508539722283e0 · report
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to_Variable_recur ringbdstack/dgib/DGIB/pytorch_net/util.py official repository ran MIT (permissive) · 0c7516590985e454 · report

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Link PredictionRepresentation Learning

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