9 accepted papers
Graph outlier detection is a critical task for identifying rare, deviant patterns in graph-structured data. However, prevalent methods based on graph convolution are fundamentally challenged by the ''Homophily Trap'': the aggregation of features from neighboring nodes inadvertently contaminates the…
A fundamental challenge in graph-level anomaly detection (GLAD) is the scarcity of anomalous graph data, as the training dataset typically contains only normal graphs or very few anomalies. This imbalance hinders the development of robust detection models. In this paper, we propose **A**nomalous **G…
Multi-view clustering (MVC) aims to integrate information from diverse data sources to facilitate the clustering process, which has achieved considerable success in various real-world applications. However, previous MVC methods typically employ one of two strategies: (1) designing separate feature e…
Identifying anomalous graphs is essential in real-world scenarios such as molecular and social network analysis, yet anomalous samples are generally scarce and unavailable. This paper proposes a Self-Discriminative Modeling (SDM) framework that trains a deep neural network only on normal graphs to d…
The convergence of graph learning and multi-view learning has propelled the emergence of multi-view graph neural networks (MGNNs), offering unprecedented capabilities to address complex real-world data characterized by heterogeneous yet interconnected information. While existing MGNNs exploit the p…
Many well-known and effective anomaly detection methods assume that a reasonable decision boundary has a hypersphere shape, which however is difficult to obtain in practice and is not sufficiently compact, especially when the data are in high-dimensional spaces. In this paper, we first propose a nov…
Graph-level clustering, which is essential in medical, biomedical, and social network data analysis, aims to group a set of graphs into various clusters. However, existing methods generally rely on a single clustering criterion, e.g., $k$-means, which limits their abilities to fully exploit the co…
Graph anomaly detection (GAD), which aims to identify those graphs that are significantly different from other ones, has gained growing attention in many real-world scenarios. However, existing GAD methods are generally designed for centralized training, while in real-world collaboration, graph data…
Recently deep learning methods have shown significant progress in data clustering tasks. Deep clustering methods (including distance-based methods and subspace-based methods) integrate clustering and feature learning into a unified framework, where there is a mutual promotion between clustering and…