- A New Dynamic Algorithm for Densest Subhypergraphs


Highlight: This algorithm worked only on unweighted hypergraphs, and had an approximation ratio of (1 +?)r2 and an update time of O(poly(r, log?n)), where r denotes the maximum rank of the input across all the updates. We obtain a new algorithm for this problem, which works even when the input hypergraph is weighted.


- TREND: TempoRal Event and Node Dynamics for Graph Representation Learning


Highlight: In this work, We propose TREND, a novel framework for temporal graph representation learning, driven by TempoRal Event and Node Dynamics and built upon a Hawkes process-based graph neural network (GNN).


- Multimodal Continual Graph Learning with Neural Architecture Search


Highlight: However, considering multimodal continual graph learning with evolving topological structures poses great challenges: i) it is unclear how to incorporate the multimodal information into continual graph learning and ii) it is nontrivial to design models that can capture the structure-evolving dynamics in continual graph learning. To tackle these challenges, in this paper we propose a novel Multimodal Structure-evolving Continual Graph Learning (MSCGL) model, which continually learns both the model architecture and the corresponding parameters for Adaptive Multimodal Graph Neural Network (AdaMGNN).


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