Sugar: Subgraph neural network with reinforcement pooling and self-supervised mutual information mechanism
Graph representation learning has attracted increasing research attention. However, most
existing studies fuse all structural features and node attributes to provide an overarching …
existing studies fuse all structural features and node attributes to provide an overarching …
Behavior-aware account de-anonymization on ethereum interaction graph
Blockchain technology has the characteristics of decentralization, traceability and tamper-
proof, which creates a reliable decentralized trust mechanism, further accelerating the …
proof, which creates a reliable decentralized trust mechanism, further accelerating the …
AvgNet: Adaptive visibility graph neural network and its application in modulation classification
Our digital world is full of time series and graphs which capture the various aspects of many
complex systems. Traditionally, there are respective methods in processing these two …
complex systems. Traditionally, there are respective methods in processing these two …
Multi-view representation model based on graph autoencoder
J Li, G Lu, Z Wu, F Ling - Information Sciences, 2023 - Elsevier
Graph representation learning is a hot topic in non-Euclidean data in various domains, such
as social networks, biological networks, etc. When some data labels are missing, graph …
as social networks, biological networks, etc. When some data labels are missing, graph …
SigNet: A novel deep learning framework for radio signal classification
Deep learning methods achieve great success in many areas due to their powerful feature
extraction capabilities and end-to-end training mechanism, and recently they are also …
extraction capabilities and end-to-end training mechanism, and recently they are also …
Identity inference on blockchain using graph neural network
The anonymity of blockchain has accelerated the growth of illegal activities and criminal
behaviors on cryptocurrency platforms. Although decentralization is one of the typical …
behaviors on cryptocurrency platforms. Although decentralization is one of the typical …
Model: Motif-based deep feature learning for link prediction
Link prediction plays an important role in network analysis and applications. Recently,
approaches for link prediction have evolved from traditional similarity-based algorithms into …
approaches for link prediction have evolved from traditional similarity-based algorithms into …
Adaptive subgraph neural network with reinforced critical structure mining
While graph representation learning methods have shown success in various graph mining
tasks, what knowledge is exploited for predictions is less discussed. This paper proposes a …
tasks, what knowledge is exploited for predictions is less discussed. This paper proposes a …
Tsgn: Transaction subgraph networks for identifying ethereum phishing accounts
Blockchain technology and, in particular, blockchain-based transaction offers us information
that has never been seen before in the financial world. In contrast to fiat currencies …
that has never been seen before in the financial world. In contrast to fiat currencies …
Time series classification based on complex network
H Li, R Jia, X Wan - Expert Systems with Applications, 2022 - Elsevier
Time series classification is an important topic in data mining. Time series classification
methods have been studied by many researchers. A feature-weighted classification method …
methods have been studied by many researchers. A feature-weighted classification method …