Zibin Zheng
Biographic Data
| ID | 3996940 |
|---|---|
| NAME | Zibin Zheng |
| GIVEN NAMES | Zibin |
| FAMILY NAME | Zheng |
| SIGNATURE | ZHENG Z |
| AFFILIATIONS | Sun Yat-sen University |
| ORCID | 0000-0001-7872-7718 |
| VERIFIED | Yes |
| TOTAL WORKS | 9 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 9 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
A Review-Level Sentiment Information Enhanced Multitask Learning Approach for Explainable Recommendation
Recommendation system plays a remarkable role in solving the problem of information overload on the Internet. Existing research demonstrates that a recommended list enclosed with appropriate explanations can enhance the transparency of the system and encourage users to make decisions. Although existing works have achieved effective results, they still suffer from at least one of the following limitations: the work either does not use sentiment in…
A General Framework for Account Risk Rating on Ethereum
As the largest blockchain platform that supports smart contracts, Ethereum has attracted wide attention from both academia and industry in recent years. Along with the prosperous development of Ethereum, the high-risk illegal practices on it are becoming more and more rampant, seriously jeopardizing the system’s trading security and long-term development. Therefore, the detection and quantification of account risk are of great importance for both…
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…
FairAGG
Graph neural networks (GNNs) have shown intrinsic topology bias inherited from graph-structured data, where a majority of nodes are associated with specific sensitive attributes (e.g., age, and race). In this regard, GNNs make discriminatory decisions toward certain groups defined by the sensitive attribute. Over the past few years, efforts have been made to mitigate the fairness issue of GNNs caused by topology bias. While achieving impressive r…
Hide and Seek
With the wide application and development of blockchain technology, the past years have witnessed the emergence of various cybercrimes, which have caused a huge amount of economic loss. Among them, phishing scams on the blockchain are regarded as a serious threat to the trading security of the blockchain ecosystem. By modeling the transaction data of blockchain as a network, a series of graph-based phishing detection frameworks have been proposed…
From Decentralization to Oligopoly
As one of the most popular blockchain systems, EOSIO has been widely used in decentralized applications (DApps). Compared with traditional proof-of-work (PoW)-based blockchain systems like Bitcoin, EOSIO achieves a high transaction throughput and an alleged decentralization with the Delegated Proof-of-Stake (DPoS) consensus protocol. However, recent reports claimed the existence of voting collusion and manipulation during the DPoS consensus proce…
Sequential Attacker–Defender Game on Complex Networks Considering the Cascading Failure Process
Cascading failure is a ubiquitous phenomenon that can paralyze networked systems in a short time. Many traditional studies of cascading failures have been conducted from the perspective of either an attacker or a defender. In reality, however, malicious attacks on networks are rarely a one-sided process. Instead, both the attacker and defender are actively involved. We use game theory to study the strategies of both sides in terms of an attacker–…
Evolution of Ethereum Transaction Relationships
Much of the current research in Ethereum transaction records focuses on the statistical analysis and measurements of existing data; however, the evolution mechanism of Ethereum transactions is an important, yet seldom discussed issue. In this work, we first collect the transaction data of Ethereum and build network models from a microlevel view and then use a link-prediction-based framework to quantify the impact of network characteristics on Eth…
Blockchain challenges and opportunities
Blockchain has numerous benefits such as decentralisation, persistency, anonymity and auditability. There is a wide spectrum of blockchain applications ranging from cryptocurrency, financial services, risk management, internet of things (IoT) to public and social services. Although a number of studies focus on using the blockchain technology in various application aspects, there is no comprehensive survey on the blockchain technology in both tech…
No prominent works on this page.
Blockchain challenges and opportunities
Blockchain has numerous benefits such as decentralisation, persistency, anonymity and auditability. There is a wide spectrum of blockchain applications ranging from cryptocurrency, financial services, risk management, internet of things (IoT) to public and social services. Although a number of studies focus on using the blockchain technology in various application aspects, there is no comprehensive survey on the blockchain technology in both tech…
Sequential Attacker–Defender Game on Complex Networks Considering the Cascading Failure Process
Cascading failure is a ubiquitous phenomenon that can paralyze networked systems in a short time. Many traditional studies of cascading failures have been conducted from the perspective of either an attacker or a defender. In reality, however, malicious attacks on networks are rarely a one-sided process. Instead, both the attacker and defender are actively involved. We use game theory to study the strategies of both sides in terms of an attacker–…
Evolution of Ethereum Transaction Relationships
Much of the current research in Ethereum transaction records focuses on the statistical analysis and measurements of existing data; however, the evolution mechanism of Ethereum transactions is an important, yet seldom discussed issue. In this work, we first collect the transaction data of Ethereum and build network models from a microlevel view and then use a link-prediction-based framework to quantify the impact of network characteristics on Eth…
Hide and Seek
With the wide application and development of blockchain technology, the past years have witnessed the emergence of various cybercrimes, which have caused a huge amount of economic loss. Among them, phishing scams on the blockchain are regarded as a serious threat to the trading security of the blockchain ecosystem. By modeling the transaction data of blockchain as a network, a series of graph-based phishing detection frameworks have been proposed…
From Decentralization to Oligopoly
As one of the most popular blockchain systems, EOSIO has been widely used in decentralized applications (DApps). Compared with traditional proof-of-work (PoW)-based blockchain systems like Bitcoin, EOSIO achieves a high transaction throughput and an alleged decentralization with the Delegated Proof-of-Stake (DPoS) consensus protocol. However, recent reports claimed the existence of voting collusion and manipulation during the DPoS consensus proce…
A Review-Level Sentiment Information Enhanced Multitask Learning Approach for Explainable Recommendation
Recommendation system plays a remarkable role in solving the problem of information overload on the Internet. Existing research demonstrates that a recommended list enclosed with appropriate explanations can enhance the transparency of the system and encourage users to make decisions. Although existing works have achieved effective results, they still suffer from at least one of the following limitations: the work either does not use sentiment in…
A General Framework for Account Risk Rating on Ethereum
As the largest blockchain platform that supports smart contracts, Ethereum has attracted wide attention from both academia and industry in recent years. Along with the prosperous development of Ethereum, the high-risk illegal practices on it are becoming more and more rampant, seriously jeopardizing the system’s trading security and long-term development. Therefore, the detection and quantification of account risk are of great importance for both…
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…
FairAGG
Graph neural networks (GNNs) have shown intrinsic topology bias inherited from graph-structured data, where a majority of nodes are associated with specific sensitive attributes (e.g., age, and race). In this regard, GNNs make discriminatory decisions toward certain groups defined by the sensitive attribute. Over the past few years, efforts have been made to mitigate the fairness issue of GNNs caused by topology bias. While achieving impressive r…
Computer Science (9 works) · Computer security (6 works) · Blockchain Technology Applications and Security (5 works) · Artificial Intelligence (4 works) · Database transaction (4 works) · Advanced Graph Neural Networks (3 works) · Adversarial Robustness in Machine Learning (3 works) · Complex Network Analysis Techniques (3 works) · Mathematics (3 works) · Artificial neural network (2 works)