GitHub

AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection

1️⃣ AnomalyGFM is the first GAD-oriented GFM with strong zero-shot and few-shot generalization abilities. 2️⃣ AnomalyGFM is pre-trained to learn discriminative, graph-agnostic class prototypes with ...
GitHub

Graph-Zero: A Hybrid Architecture for Robust and Efficient Long-Term Memory in AI Agents

最稳健的AI记忆系统，其设计灵感往往源于对人类认知模式的模拟 ...
IEEE

Learning Generalizable Contrastive Representations for Graph Zero-Shot Learning

Abstract: This paper studies the problem of graph zero-shot learning, which aims at recognizing novel classes of nodes on the graph that are never seen during training. The key to graph zero-shot ...
Frontiers

TP-GCL: graph contrastive learning from the tensor perspective

1 College of Computer, Qinghai Normal University, Xining, China 2 The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Xining, China Graph Neural Networks (GNNs) ...
marktechpost

Higher-Order Guided Diffusion for Graph Generation: A Coarse-to-Fine Approach to Preserving Topological Structures

Graph generation is a complex problem that involves constructing structured, non-Euclidean representations while maintaining meaningful relationships between entities. Most current methods fail to ...
EurekAlert!

Exploring & exploiting high-order graph structure for sparse knowledge graph completion

High sparse Knowledge Graph is a key challenge to solve the Knowledge Graph Completion task. Due to the sparsity of the KGs, there are not enough first-order neighbors to learn the features of ...
IEEE

PCHGCN: Physically Constrained Higher-Order Graph Convolutional Network for Pedestrian Trajectory Prediction

Abstract: Pedestrian trajectory prediction is crucial in fields, such as autonomous driving and robot navigation, but it remains challenging due to the complex social interactions among pedestrians.