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Found 10 papers, 1 papers with code
Topology Imbalance and Relation Inauthenticity Aware Hierarchical Graph Attention Networks for Fake News Detection
However, little attention is paid to the issues of both authenticity of the relationships and topology imbalance in the structure of NPG, which trick existing methods and thus lead to incorrect prediction results.
Federated Deep Subspace Clustering
In each client, there is a deep subspace clustering network accounting for grouping the isolated data, composed of a encode network, a self-expressive layer, and a decode network.
Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents
In the first stage, agents analyze their respective schema and communicate with each other to collect the schema information relevant to the question.
Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality
Then STRIPE incorporates separate spatial and temporal memory networks to capture and store prototypes of normal patterns, respectively.
Federated learning-outcome prediction with multi-layer privacy protection
To this end, this study proposes a distributed grade prediction model, dubbed FecMap, by exploiting the federated learning (FL) framework that preserves the private data of local clients and communicates with others through a global generalized model.
Concept Prerequisite Relation Prediction by Using Permutation-Equivariant Directed Graph Neural Networks
This paper studies the problem of CPRP, concept prerequisite relation prediction, which is a fundamental task in using AI for education.
SGOOD: Substructure-enhanced Graph-Level Out-of-Distribution Detection
We find that substructure differences commonly exist between ID and OOD graphs, and design SGOOD with a series of techniques to encode task-agnostic substructures for effective OOD detection.
BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection
By swapping the context embeddings between nodes and edges and measuring the agreement in the embedding space, we enable the mutual detection of node and edge anomalies.
Imbalanced Node Classification Beyond Homophilic Assumption
minority classes to balance the label and topology distribution.
Deep Feature Learning of Multi-Network Topology for Node Classification
Network Embedding, aiming to learn non-linear and low-dimensional feature representation based on network topology, has been proved to be helpful on tasks of network analysis, especially node classification.
