Qi Xuan
Biographic Data
| ID | 8800879 |
|---|---|
| NAME | Qi Xuan |
| GIVEN NAMES | Qi |
| FAMILY NAME | Xuan |
| SIGNATURE | XUAN Q |
| AFFILIATIONS | Zhejiang University of Technology |
| ORCID | 0000-0002-1042-470X |
| VERIFIED | Yes |
| TOTAL WORKS | 12 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 12 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Structural characteristics and community formation mechanisms in aquatic plant communities during the near-natural restoration of urban rivers
Mutual Influence in Citation and Cooperation Patterns
Measuring the influence of scientists and their activities on science and society is important and indeed essential for many studies. Despite the substantial efforts devoted to exploring the influence’s measures and patterns of an individual scientific enterprise, it remains unclear how to quantify the mutual impact of multiple scientific activities. This work quantifies the relationship between the scientists’ interactive activities and their in…
DeepInsight
With the rapid development of artificial intelligence, a number of machine learning algorithms, such as graph neural networks (GNNs), have been proposed to facilitate network analysis or graph data mining. Although effective, recent studies show that these advanced methods may suffer from adversarial attacks, i.e., they may lose effectiveness when only a small fraction of links are unexpectedly changed. This article investigates three well-known …
Node Injection Attack Based on Label Propagation Against Graph Neural Network
Graph neural network (GNN) has achieved remarkable success in various graph learning tasks, such as node classification, link prediction, and graph classification. The key to the success of GNN lies in its effective structure information representation through neighboring aggregation. However, the attacker can easily perturb the aggregation process through injecting fake nodes, which reveals that GNN is vulnerable to the graph injection attack (G…
Gani
Graph neural networks (GNNs) have found successful applications in various graph-related tasks. However, recent studies have shown that many GNNs are vulnerable to adversarial attacks. In a vast majority of existing studies, adversarial attacks on GNNs are launched via direct modification of the original graph such as adding/removing links, which may not be applicable in practice. In this article, we focus on a realistic attack operation via inje…
GA-Based Multipopulation Synergistic Gene Screening Strategy on Critical Nodes Detection
Critical node detection (CND) is commonly used to detect nodes with a high impact on network robustness. It has been widely used in disease propagation, social networks, communications, and other fields. As a nondeterministic polynomial-time (NP)-complete problem, the efficiency of solving CND severely limits the scale of the available network. Fortunately, the evolutionary algorithm (EA) is an effective method to solve this problem. However, alt…
Single-Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning
Graph neural networks (GNNs) have achieved remarkable success in various real-world applications. However, recent studies highlight the vulnerability of GNNs to malicious perturbations. Previous adversaries primarily focus on graph modifications or node injections to existing graphs, yielding promising results but with notable limitations. Graph modification attack (GMA) requires manipulation of the original graph, which is often impractical, whi…
Attacking the Core Structure of Complex Network
The concept of $k$ -core in complex networks plays a key role in many applications, e.g., understanding the global structure or identifying central/critical nodes, of a network. A malicious attacker with a jamming ability can exploit the vulnerability of the $k$ -core structure to attack the network and invalidate the network analysis methods, e.g., reducing the $k$ -shell values of nodes can deceive graph algorithms, leading to the wrong decisio…
Time-Series Snapshot Network for Partner Recommendation
The last decade has witnessed the rapid growth of open-source software (OSS). Still, all contributors may find it difficult to assimilate into the OSS community even they are enthusiastic to make contributions. We thus suggest that partner recommendation across different roles may benefit both the users and developers, i.e., once we are able to make successful recommendation for those in need, it may dramatically contribute to the productivity of…
Multiscale Evolutionary Perturbation Attack on Community Detection
Community detection, aiming to group nodes based on their connections, plays an important role in network analysis since communities, treated as meta-nodes, allow us to create a large-scale map of a network to simplify its analysis. However, for privacy reasons, we may want to prevent communities from being discovered in certain cases, leading to the topics on community deception. In this article, we formalize this community detection attack prob…
Mga
The adversarial attack methods based on gradient information can adequately find the perturbations, that is, the combinations of rewired links, thereby reducing the effectiveness of the deep learning model-based graph embedding algorithms, but it is also easy to fall into a local optimum. Therefore, this article proposes a momentum gradient attack (MGA) against the graph convolutional network (GCN) model, which can achieve more aggressive attacks…
Smoothing Adversarial Training for GNN
Recently, a graph neural network (GNN) was proposed to analyze various graphs/networks, which has been proven to outperform many other network analysis methods. However, it is also shown that such state-of-the-art methods suffer from adversarial attacks, i.e., carefully crafted adversarial networks with slight perturbation on clean one may invalid these methods on lots of applications, such as network embedding, node classification, link predicti…
No prominent works on this page.
Multiscale Evolutionary Perturbation Attack on Community Detection
Community detection, aiming to group nodes based on their connections, plays an important role in network analysis since communities, treated as meta-nodes, allow us to create a large-scale map of a network to simplify its analysis. However, for privacy reasons, we may want to prevent communities from being discovered in certain cases, leading to the topics on community deception. In this article, we formalize this community detection attack prob…
Mga
The adversarial attack methods based on gradient information can adequately find the perturbations, that is, the combinations of rewired links, thereby reducing the effectiveness of the deep learning model-based graph embedding algorithms, but it is also easy to fall into a local optimum. Therefore, this article proposes a momentum gradient attack (MGA) against the graph convolutional network (GCN) model, which can achieve more aggressive attacks…
Smoothing Adversarial Training for GNN
Recently, a graph neural network (GNN) was proposed to analyze various graphs/networks, which has been proven to outperform many other network analysis methods. However, it is also shown that such state-of-the-art methods suffer from adversarial attacks, i.e., carefully crafted adversarial networks with slight perturbation on clean one may invalid these methods on lots of applications, such as network embedding, node classification, link predicti…
Time-Series Snapshot Network for Partner Recommendation
The last decade has witnessed the rapid growth of open-source software (OSS). Still, all contributors may find it difficult to assimilate into the OSS community even they are enthusiastic to make contributions. We thus suggest that partner recommendation across different roles may benefit both the users and developers, i.e., once we are able to make successful recommendation for those in need, it may dramatically contribute to the productivity of…
Attacking the Core Structure of Complex Network
The concept of $k$ -core in complex networks plays a key role in many applications, e.g., understanding the global structure or identifying central/critical nodes, of a network. A malicious attacker with a jamming ability can exploit the vulnerability of the $k$ -core structure to attack the network and invalidate the network analysis methods, e.g., reducing the $k$ -shell values of nodes can deceive graph algorithms, leading to the wrong decisio…
Mutual Influence in Citation and Cooperation Patterns
Measuring the influence of scientists and their activities on science and society is important and indeed essential for many studies. Despite the substantial efforts devoted to exploring the influence’s measures and patterns of an individual scientific enterprise, it remains unclear how to quantify the mutual impact of multiple scientific activities. This work quantifies the relationship between the scientists’ interactive activities and their in…
DeepInsight
With the rapid development of artificial intelligence, a number of machine learning algorithms, such as graph neural networks (GNNs), have been proposed to facilitate network analysis or graph data mining. Although effective, recent studies show that these advanced methods may suffer from adversarial attacks, i.e., they may lose effectiveness when only a small fraction of links are unexpectedly changed. This article investigates three well-known …
Node Injection Attack Based on Label Propagation Against Graph Neural Network
Graph neural network (GNN) has achieved remarkable success in various graph learning tasks, such as node classification, link prediction, and graph classification. The key to the success of GNN lies in its effective structure information representation through neighboring aggregation. However, the attacker can easily perturb the aggregation process through injecting fake nodes, which reveals that GNN is vulnerable to the graph injection attack (G…
Gani
Graph neural networks (GNNs) have found successful applications in various graph-related tasks. However, recent studies have shown that many GNNs are vulnerable to adversarial attacks. In a vast majority of existing studies, adversarial attacks on GNNs are launched via direct modification of the original graph such as adding/removing links, which may not be applicable in practice. In this article, we focus on a realistic attack operation via inje…
GA-Based Multipopulation Synergistic Gene Screening Strategy on Critical Nodes Detection
Critical node detection (CND) is commonly used to detect nodes with a high impact on network robustness. It has been widely used in disease propagation, social networks, communications, and other fields. As a nondeterministic polynomial-time (NP)-complete problem, the efficiency of solving CND severely limits the scale of the available network. Fortunately, the evolutionary algorithm (EA) is an effective method to solve this problem. However, alt…
Single-Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning
Graph neural networks (GNNs) have achieved remarkable success in various real-world applications. However, recent studies highlight the vulnerability of GNNs to malicious perturbations. Previous adversaries primarily focus on graph modifications or node injections to existing graphs, yielding promising results but with notable limitations. Graph modification attack (GMA) requires manipulation of the original graph, which is often impractical, whi…
Structural characteristics and community formation mechanisms in aquatic plant communities during the near-natural restoration of urban rivers
Computer Science (10 works) · Advanced Graph Neural Networks (8 works) · Artificial Intelligence (7 works) · Theoretical Computer Science (7 works) · Graph (6 works) · Complex Network Analysis Techniques (5 works) · Engineering (5 works) · Machine learning (5 works) · Artificial neural network (4 works) · Computer network (4 works)