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Found 9 papers, 6 papers with code
Exploring Edge Probability Graph Models Beyond Edge Independency: Concepts, Analyses, and Algorithms
Desirable random graph models (RGMs) should (i) be tractable so that we can compute and control graph statistics, and (ii) generate realistic structures such as high clustering (i. e., high subgraph densities).
Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More
Then, for various conditions commonly involved in different CO problems, we derive nontrivial objectives and derandomization to meet the targets.
HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs
Based on the generative SSL task, we propose a hypergraph SSL method, HypeBoy.
Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective
Second, how does A-X dependence affect GNNs?
Robust Graph Clustering via Meta Weighting for Noisy Graphs
We add a learnable weight to each node pair, and MetaGC adaptively adjusts the weights of node pairs using meta-weighting so that the weights of meaningful node pairs increase and the weights of less-meaningful ones (e. g., noise edges) decrease.
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
To fill this gap, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 23 state-of-the-art methods and more than 20 CO problems.
Towards Deep Attention in Graph Neural Networks: Problems and Remedies
AERO-GNN provably mitigates the proposed problems of deep graph attention, which is further empirically demonstrated with (a) its adaptive and less smooth attention functions and (b) higher performance at deep layers (up to 64).
Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNs
In particular, inspired by a numerical integration method on manifolds called Feedback Integrators, we propose to instantiate it on the tangent bundle of the Stiefel manifold for the first time.
Double Prioritized State Recycled Experience Replay
Experience replay enables online reinforcement learning agents to store and reuse the previous experiences of interacting with the environment.
