Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approach
CoRR(2024)
摘要
Recent studies successfully learned static graph embeddings that are
structurally fair by preventing the effectiveness disparity of high- and
low-degree vertex groups in downstream graph mining tasks. However, achieving
structure fairness in dynamic graph embedding remains an open problem.
Neglecting degree changes in dynamic graphs will significantly impair embedding
effectiveness without notably improving structure fairness. This is because the
embedding performance of high-degree and low-to-high-degree vertices will
significantly drop close to the generally poorer embedding performance of most
slightly changed vertices in the long-tail part of the power-law distribution.
We first identify biased structural evolutions in a dynamic graph based on the
evolving trend of vertex degree and then propose FairDGE, the first
structurally Fair Dynamic Graph Embedding algorithm. FairDGE learns biased
structural evolutions by jointly embedding the connection changes among
vertices and the long-short-term evolutionary trend of vertex degrees.
Furthermore, a novel dual debiasing approach is devised to encode fair
embeddings contrastively, customizing debiasing strategies for different biased
structural evolutions. This innovative debiasing strategy breaks the
effectiveness bottleneck of embeddings without notable fairness loss. Extensive
experiments demonstrate that FairDGE achieves simultaneous improvement in the
effectiveness and fairness of embeddings.
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