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Found 10 papers, 5 papers with code
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning
Subgraph federated learning (subgraph-FL) is a new distributed paradigm that facilitates the collaborative training of graph neural networks (GNNs) by multi-client subgraphs.
Rethinking Node-wise Propagation for Large-scale Graph Learning
However, (i) Most scalable GNNs tend to treat all nodes in graphs with the same propagation rules, neglecting their topological uniqueness; (ii) Existing node-wise propagation optimization strategies are insufficient on web-scale graphs with intricate topology, where a full portrayal of nodes' local properties is required.
Towards Effective and General Graph Unlearning via Mutual Evolution
With the rapid advancement of AI applications, the growing needs for data privacy and model robustness have highlighted the importance of machine unlearning, especially in thriving graph-based scenarios.
LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning
Most existing graph neural networks (GNNs) are limited to undirected graphs, whose restricted scope of the captured relational information hinders their expressive capabilities and deployments in real-world scenarios.
FedGTA: Topology-aware Averaging for Federated Graph Learning
Existing FGL studies fall into two categories: (i) FGL Optimization, which improves multi-client training in existing machine learning models; (ii) FGL Model, which enhances performance with complex local models and multi-client interactions.
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity
Then, each client conducts personalized training based on the local subgraph and the federated knowledge extractor.
Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification
Despite numerous attempts, most existing GNNs struggle to achieve optimal node representations due to the constraints of undirected graphs.
Ranked #28 on
Node Classification
on Cornell
Locally Differentially Private Graph Embedding
We propose LDP-GE, a novel privacy-preserving graph embedding framework, to protect the privacy of node data.
Scalable and Effective Conductance-based Graph Clustering
Despite the significant success of conductance-based graph clustering, existing algorithms are either hard to obtain satisfactory clustering qualities, or have high time and space complexity to achieve provable clustering qualities.
On Random Walk Based Graph Sampling
In this paper, we first present a comprehensive analysis of the drawbacks of three widely-used random walk based graph sampling algorithms, called re-weighted random walk (RW) algorithm, Metropolis-Hastings random walk (MH) algorithm and maximum-degree random walk (MD) algorithm.
