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Zhang-Hua Fu

6 accepted papers

2021

A New Upper Bound Based on Vertex Partitioning for the Maximum K-plex Problem

IJCAI 2021poster

Given an undirected graph, the Maximum k-plex Problem (MKP) is to find a largest induced subgraph in which each vertex has at most k−1 non-adjacent vertices. The problem arises in social network analysis and has found applications in many important areas employing graph-based data mining. Existing e…

2021

An Efficient Parallel Self-assembly Planning Algorithm for Modular Robots in Environments with Obstacles

ICRA 2021poster

Self-assembly has attracted growing interests in modular robotics during past decades. Recent work accelerates the assembly process by parallelizing the docking actions among robots. However, these methods can only apply to ideal environments without obstacles. Otherwise, robots will get trapped dur…

Cited by 7SourceScholar
2021

Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances

AAAI 2021technical

For the traveling salesman problem (TSP), the existing supervised learning based algorithms suffer seriously from the lack of generalization ability. To overcome this drawback, this paper tries to train (in supervised manner) a small-scale model, which could be repetitively used to build heat maps f…

2021

Improving Maximum k-plex Solver via Second-Order Reduction and Graph Color Bounding

AAAI 2021technical

In a graph, a k-plex is a vertex set in which every vertex is not adjacent to at most k vertices of this set. The maximum k-plex problem, which asks for the largest k-plex from the given graph, is a key primitive in a variety of real-world applications like community detection and so on. In the pape…

2021

Knowledge Refinery: Learning from Decoupled Label

AAAI 2021technical

Recently, a variety of regularization techniques have been widely applied in deep neural networks, which mainly focus on the regularization of weight parameters to encourage generalization effectively. Label regularization techniques are also proposed with the motivation of softening the labels whil…

Cited by 15SourcePDFScholar
2020

Communicative Representation Learning on Attributed Molecular Graphs

IJCAI 2020poster

Constructing proper representations of molecules lies at the core of numerous tasks such as molecular property prediction and drug design. Graph neural networks, especially message passing neural network (MPNN) and its variants, have recently made remarkable achievements in molecular graph modeling.…