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Jiexin Lu

2 accepted papers

2024

CONC: Complex-noise-resistant Open-set Node Classification with Adaptive Noise Detection

IJCAI 2024poster

As a popular task in graph learning, node classification seeks to assign labels to nodes, taking into account both their features and connections. However, an important challenge for its application in real-world scenarios is the presence of newly-emerged out-of-distribution samples and noisy sample…

Cited by 1SourcePDFScholar
2024

ROG_PL: Robust Open-Set Graph Learning via Region-Based Prototype Learning

AAAI 2024technical

Open-set graph learning is a practical task that aims to classify the known class nodes and to identify unknown class samples as unknowns. Conventional node classification methods usually perform unsatisfactorily in open-set scenarios due to the complex data they encounter, such as out-of-distributi…

Cited by 2SourcePDFScholar