KEYWORDS: Performance modeling, Matrices, Data modeling, Neural networks, Feature extraction, Social networks, Information fusion, Education and training, Superposition, Statistical modeling
Graph anomaly detection plays a key role in many real-world scenarios such as social network flooding detection and financial fraud detection. Graph anomaly detection methods based on contrastive self-supervision have been proven to be effective, but current models lack full utilization of features and lack attention to the over-smoothing problem during training. Therefore, this paper proposes a new contrastive self-supervised model for sampled node and subgraph instance pairs, which fully captures the information of nodes and subgraphs, and at the same time reconstructs the information of subgraphs, which effectively solves the problems of insufficient feature capture and over-smoothing of graph neural network modules. Experiments on three public datasets and three benchmark models demonstrate the superiority of our proposed model.
There are fraudulent promotion behaviors in GitHub, which promotes Stars and Forks for specific repositories. It is harmful to the environment of the open source community, while it is not effectively detected by GitHub yet. This paper applies a heterogeneous neural network to detect repositories that are suspected of fraudulent promotion behavior. A heterogenous mini-graph neural network with attention mechanism and hyper-graph generation is proposed to detect repositories with cheating behaviors. Attention mechanism can dynamically balance the weight of semantics in heterogeneous information networks. Hyper-graph generation method can solve the problem of poor connectivity caused by many small graphs in the dataset. The experimental result shows that the model can effectively detect this kind of cheating behavior.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.