Open Access Issue
Few-Shot Graph Classification with Structural-Enhanced Contrastive Learning for Graph Data Copyright Protection
Tsinghua Science and Technology 2024, 29 (2): 605-616
Published: 22 September 2023
Abstract PDF (7.7 MB) Collect

Open-source licenses can promote the development of machine learning by allowing others to access, modify, and redistribute the training dataset. However, not all open-source licenses may be appropriate for data sharing, as some may not provide adequate protections for sensitive or personal information such as social network data. Additionally, some data may be subject to legal or regulatory restrictions that limit its sharing, regardless of the licensing model used. Hence, obtaining large amounts of labeled data can be difficult, time-consuming, or expensive in many real-world scenarios. Few-shot graph classification, as one application of meta-learning in supervised graph learning, aims to classify unseen graph types by only using a small amount of labeled data. However, the current graph neural network methods lack full usage of graph structures on molecular graphs and social network datasets. Since structural features are known to correlate with molecular properties in chemistry, structure information tends to be ignored with sufficient property information provided. Nevertheless, the common binary classification task of chemical compounds is unsuitable in the few-shot setting requiring novel labels. Hence, this paper focuses on the graph classification tasks of a social network, whose complex topology has an uncertain relationship with its nodes’ attributes. With two multi-class graph datasets with large node-attribute dimensions constructed to facilitate the research, we propose a novel learning framework that integrates both meta-learning and contrastive learning to enhance the utilization of graph topological information. Extensive experiments demonstrate the competitive performance of our framework respective to other state-of-the-art methods.

Open Access Issue
Link-Privacy Preserving Graph Embedding Data Publication with Adversarial Learning
Tsinghua Science and Technology 2022, 27 (2): 244-256
Published: 29 September 2021
Abstract PDF (10.1 MB) Collect

The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods, aiming at learning a continuous vector space for the graph, which is amenable to be adopted in traditional machine learning algorithms in favor of vector representations. Graph embedding methods build an important bridge between social network analysis and data analytics, as social networks naturally generate an unprecedented volume of graph data continuously. Publishing social network data not only brings benefit for public health, disaster response, commercial promotion, and many other applications, but also gives birth to threats that jeopardize each individual’s privacy and security. Unfortunately, most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social networks. To be specific, attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network embedding. In this paper, we propose a novel link-privacy preserved graph embedding framework using adversarial learning, which can reduce adversary’s prediction accuracy on sensitive links, while persevering sufficient non-sensitive information, such as graph topology and node attributes in graph embedding. Extensive experiments are conducted to evaluate the proposed framework using ground truth social network datasets.

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