Search Results for author:
Found 9 papers, 1 papers with code
FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework
Although federated learning offers a promising solution, the prevalent non-IID problems and high communication costs present significant challenges, particularly pronounced in collaborations with graph data distributed among different participants.
MULTI: Multimodal Understanding Leaderboard with Text and Images
Rapid progress in multimodal large language models (MLLMs) highlights the need to introduce challenging yet realistic benchmarks to the academic community, while existing benchmarks primarily focus on understanding simple natural images and short context.
Self-Discriminative Modeling for Anomalous Graph Detection
Under the framework, we provide three algorithms with different computational efficiencies and stabilities for anomalous graph detection.
Value Iteration Networks with Gated Summarization Module
We propose a novel approach, Value Iteration Networks with Gated Summarization Module (GS-VIN), which incorporates two main improvements: (1) employing an Adaptive Iteration Strategy in the Value Iteration module to reduce the number of iterations, and (2) introducing a Gated Summarization module to summarize the iterative process.
Deep Graph-Level Orthogonal Hypersphere Compression for Anomaly Detection
Graph-level anomaly detection aims to identify anomalous graphs from a collection of graphs in an unsupervised manner.
Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network
In this work, we study the problem of partitioning a set of graphs into different groups such that the graphs in the same group are similar while the graphs in different groups are dissimilar.
Unsupervised Deep Discriminant Analysis Based Clustering
This work presents an unsupervised deep discriminant analysis for clustering.
Perturbation Learning Based Anomaly Detection
This paper presents a simple yet effective method for anomaly detection.
Efficient Deep Embedded Subspace Clustering
The proposed method is out of the self-expressive framework, scales to the sample size linearly, and is applicable to arbitrarily large datasets and online clustering scenarios.
