22 citations · 89 across the 36 of their papers we have counts for
6 papers · 1 filter
MGNNI: Multiscale Graph Neural Networks with Implicit Layers
Juncheng Liu, Bryan Hooi, Kenji Kawaguchi +1
Recently, implicit graph neural networks (GNNs) have been proposed to capture long-range dependencies in underlying graphs. In this paper, we introduce and justify two weaknesses o…
Flashlight: Scalable Link Prediction with Effective Decoders
Yiwei Wang, Bryan Hooi, Yozen Liu +3
Link prediction (LP) has been recognized as an important task in graph learning with its broad practical applications. A typical application of LP is to retrieve the top scoring ne…
GRAPHCACHE: Message Passing as Caching for Sentence-Level Relation Extraction
Yiwei Wang, Muhao Chen, Wenxuan Zhou +3
Entity types and textual context are essential properties for sentence-level relation extraction (RE). Existing work only encodes these properties within individual instances, whic…
Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis
Yiwei Wang, Muhao Chen, Wenxuan Zhou +6
Recent literature focuses on utilizing the entity information in the sentence-level relation extraction (RE), but this risks leaking superficial and spurious clues of relations. As…
Dangling-Aware Entity Alignment with Mixed High-Order Proximities
Juncheng Liu, Zequn Sun, Bryan Hooi +5
We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem. As different KGs are naturally constructed by different sets of…
EIGNN: Efficient Infinite-Depth Graph Neural Networks
Juncheng Liu, Kenji Kawaguchi, Bryan Hooi +2
Graph neural networks (GNNs) are widely used for modelling graph-structured data in numerous applications. However, with their inherently finite aggregation layers, existing GNN mo…