Zibin Zheng

Zibin Zheng
Sun Yat-Sen University | SYSU · Department of software Engineering

PhD

About

378
Publications
550,690
Reads
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16,204
Citations
Additional affiliations
April 2018 - August 2019
Sun Yat-Sen University
Position
  • Professor

Publications

Publications (378)
Article
In the field of time series forecasting, deep learning and dynamics-based methods are two main research directions. The former focuses on the temporal information of the data while the latter emphasizes on the spatial information of the data, and rare methods combine the two information properly. In order to make better use of the information in th...
Conference Paper
Full-text available
Federated Learning (FL) suffers from Low-quality model training in mobile edge computing, due to the dynamic environment of mobile clients. To the best of our knowledge, most FL frameworks follow the \textit{reactive} client scheduling, in which the FL parameter server selects participants according to the currently-observed state of clients. Thus,...
Article
Federated learning is increasingly attractive, however as the number of training samples on a single device is too small and the training tasks of the devices are different, it faces the few‐shot multitask learning problem. Moreover, federated learning frameworks are usually vulnerable to malicious attacks of the central server and diverse clients....
Article
Reciprocal Recommender Systems (RRSs) are recommender systems specifically designed for people-to-people recommendation tasks, e.g., online gaming, dating, and recruitment services. They are fundamentally different from the conventional user–item recommendations. In RRSs, user interactions are usually directional, i.e., they are initiated by one si...
Preprint
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery. Despite these progresses, how to ensure various deep graph learning algorithms behave in a socially responsible manner and meet regulatory compliance requirements becomes an emerg...
Preprint
We present masked graph autoencoder (MaskGAE), a self-supervised learning framework for graph-structured data. Different from previous graph autoencoders (GAEs), MaskGAE adopts masked graph modeling (MGM) as a principled pretext task: masking a portion of edges and attempting to reconstruct the missing part with partially visible, unmasked graph st...
Conference Paper
Full-text available
State-of-the-art blockchain sharding solutions, say Monoxide, can induce imbalanced transaction (TX) distributions among all blockchain shards due to their account deployment mechanisms. Imbalanced TX distributions can then cause hot shards, in which the cross-shard TXs may experience an unlimited length of confirmation latency. Thus, how to addres...
Preprint
In recent years, the rise of deep learning and automation requirements in the software industry has elevated Intelligent Software Engineering to new heights. The number of approaches and applications in code understanding is growing, with deep learning techniques being used in many of them to better capture the information in code data. In this sur...
Preprint
Recently, graph convolutional networks (GCNs) have shown to be vulnerable to small adversarial perturbations, which becomes a severe threat and largely limits their applications in security-critical scenarios. To mitigate such a threat, considerable research efforts have been devoted to increasing the robustness of GCNs against adversarial attacks....
Preprint
Full-text available
This work concerns the evolutionary approaches to distributed stochastic black-box optimization, in which each worker can individually solve an approximation of the problem with nature-inspired algorithms. We propose a distributed evolution strategy (DES) algorithm grounded on a proper modification to evolution strategies, a family of classic evolu...
Article
To solve the incompleteness problem in temporal knowledge graphs (TKGs) and to discover the new knowledge, TKG completion remains an essential task always solved by graph embedding technology. Existing TKG completion methods encode time at only single granularity, which is insufficient in exploiting the rich information of distinct time granulariti...
Article
Deep reinforcement learning (DRL), which highly depends on the data representation, has shown its potential in many practical decision-making problems. However, the process of acquiring representations in DRL is easily affected by interference from models, and moreover leaves unnecessary parameters, leading to control performance reduction. In this...
Preprint
Full-text available
In this brief, we conduct a complex-network analysis of the Bitcoin transaction network. In particular, we design a new sampling method, namely random walk with flying-back (RWFB), to conduct effective data sampling. We then conduct a comprehensive analysis of the Bitcoin network in terms of the degree distribution, clustering coefficient, the shor...
Preprint
Full-text available
This work provides an efficient sampling method for the covariance matrix adaptation evolution strategy (CMA-ES) in large-scale settings. In contract to the Gaussian sampling in CMA-ES, the proposed method generates mutation vectors from a mixture model, which facilitates exploiting the rich variable correlations of the problem landscape within a l...
Article
Multiview dictionary learning (DL) is attracting attention in multiview clustering due to the efficient feature learning ability. However, most existing multiview DL algorithms are facing problems in fully utilizing consistent and complementary information simultaneously in the multiview data and learning the most precise representation for multivi...
Article
Full-text available
Natural gradient method provides a powerful paradigm for training statistical models and offers several appealing theoretic benefits. It constructs the Fisher information matrix to correct the ordinary gradients, and thus, the cost may become prohibitively expensive in the large-scale setting. This paper proposes a quasi-natural gradient method to...
Article
Since user-item interactions in recommender systems can be naturally modeled as a bipartite graph, recent studies have started to incorporate graph neural networks (GNNs) to learn user and item representations. However, existing GNN-based models for recommendation usually emphasize the graph structure but neglect the rich node (i.e., users and item...
Preprint
Full-text available
Being the largest Initial Coin Offering project, EOSIO has attracted great interest in cryptocurrency markets. Despite its popularity and prosperity (e.g., 26,311,585,008 token transactions occurred from June 8, 2018 to Aug. 5, 2020), there is almost no work investigating the EOSIO token ecosystem. To fill this gap, we are the first to conduct a sy...
Preprint
Full-text available
Recently, many Delegated Proof-of-Stake (DPoS)-based blockchains have been widely used in decentralized applications, such as EOSIO, Tron, and Binance Smart Chain. Compared with traditional PoW-based blockchain systems, these systems achieve a higher transaction throughput and are well adapted to large-scale scenes in daily applications. Decentrali...
Preprint
Full-text available
Backdoor attacks have been widely studied to hide the misclassification rules in the normal models, which are only activated when the model is aware of the specific inputs (i.e., the trigger). However, despite their success in the conventional Euclidean space, there are few studies of backdoor attacks on graph structured data. In this paper, we pro...
Preprint
Full-text available
Security incidents such as scams and hacks, have become a major threat to the health of the blockchain ecosystem, causing billions of dollars in losses each year for blockchain users. To reveal the real-world entities behind the pseudonymous blockchain account and recover the stolen funds from the massive transaction data, much effort has been devo...
Article
Full-text available
This paper studies the PBFT-based sharded permissioned blockchain, which executes in either a local datacenter or a rented cloud platform. In such permissioned blockchain, the transaction (TX) assignment strategy could be malicious such that the network shards may possibly receive imbalanced transactions or even bursty-TX injection attacks. An imba...
Preprint
Full-text available
(Please download the latest version of this survey article from arXiv: https://arxiv.org/abs/2201.03201 ) As the latest buzzword, Metaverse has attracted great attention from both industry and academia. Metaverse seamlessly integrates the real world with the virtual world and allows avatars to carry out rich activities including creation, display,...
Article
Stock movement prediction is a critical issue in the field of financial investment. It is very challenging since a stock usually shows highly stochastic property in price and has complex relationships with other stocks. Most existing approaches cannot jointly take the above two issues into account and thus cannot yield satisfactory prediction resul...
Article
Full-text available
Code review is one of the common activities to guarantee the reliability of software, while code review is time-consuming as it requires reviewers to inspect the source code of each patch. A patch may be reviewed more than once before it is eventually merged or abandoned, and then such a patch may tighten the development schedule of the developers...
Article
Full-text available
As the latest buzzword, Metaverse has attracted great attention from both industry and academia. Metaverse seamlessly integrates the real world with the virtual world and allows avatars to carry out rich activities including creation, display, entertainment, social networking, and trading. Thus, it is promising to build an exciting digital world an...
Article
Social data produced from widely emerged social media activities are expected to promote information dissemination and engagement, or even make business intelligence more powerful. However, the recent increase in social media incidents of illegal surveillance and data breaches raises questions about the current data ownership model, in which centra...
Article
In this brief, we conduct a complex-network analysis of the Bitcoin transaction network. In particular, we design a new sampling method, namely random walk with flying-back (RWFB), to conduct effective data sampling. We then conduct a comprehensive analysis of the Bitcoin network in terms of the degree distribution, clustering coefficient, the shor...
Article
Full-text available
Graph convolutional networks (GCNs), an emerging type of neural network model on graphs, have presented state-of-the-art performance on the node classification task. However, recent studies show that neural networks are vulnerable to the small but deliberate perturbations on input features. And GCNs could be more sensitive to the perturbations sinc...
Article
Unmanned aerial vehicles (UAVs) can be leveraged in mobile crowdsensing (MCS) to conduct sensing tasks at remote or rural areas through computation offloading and data sensing. Nonetheless, both computation offloading and data sensing have been separately investigated in most existing studies. In this paper, we propose a novel cooperative data sens...
Article
Graph convolutional networks (GCNs) have achieved great success in many applications and have caught significant attention in both academic and industrial domains. However, repeatedly employing graph convolutional layers would render the node embeddings indistinguishable. For the sake of avoiding oversmoothing, most GCN-based models are restricted...
Article
Heterogeneous graphs with multiple types of nodes and edges are ubiquitous in the real world and possess immense value in many graph-based downstream applications. However, the heterogeneity within nodes and edges in heterogeneous graphs has brought pressing challenges for practical node representation learning. Existing works manually define multi...
Research Proposal
Full-text available
To contribute, authors should submit their papers to the Special Track on Blockchain (organized jointly with the inaugural IEEE Symposium on Blockchain), 2021 IEEE International Conference on Smart Data Services (SMDS). ================================================================== The inaugural IEEE Symposium on Blockchain at IEEE SERVICES 202...
Article
Recently, graph convolutional networks (GCNs) have been applied to heterogeneous information network (HIN) learning and have shown promising performance. However, the performance of GCNs degrades attributed to the recursive propagation, which leads to an indistinguishable embedding for the distinctly heterogeneous node. Besides, the inherently coup...
Article
Meta‐path‐based random walk strategy has attracted tremendous attention in heterogeneous network representation, which can capture network semantics with heterogeneous neighborhoods of nodes. Despite the success of meta‐path‐based random walk strategy in plain heterogeneous networks which contain no attributes, it remains unexplored how meta‐path‐b...
Preprint
Recent studies have shown that Graph Convolutional Networks (GCNs) are vulnerable to adversarial attacks on the graph structure. Although multiple works have been proposed to improve their robustness against such structural adversarial attacks, the reasons for the success of the attacks remain unclear. In this work, we theoretically and empirically...
Article
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...
Conference Paper
Recent studies have shown that Graph Convolutional Networks (GCNs) are vulnerable to adversarial attacks on the graph structure. Although multiple works have been proposed to improve their robustness against such structural adversarial attacks, the reasons for the success of the attacks remain unclear. In this work, we theoretically and empirically...
Article
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...
Conference Paper
Full-text available
In a large-scale sharded blockchain, transactions are processed by a number of parallel committees collaboratively. Thus, the blockchain throughput can be strongly boosted. A problem is that some groups of blockchain nodes consume large latency to form committees at the beginning of each epoch. Furthermore, the heterogeneous processing capabilities...
Conference Paper
Full-text available
Bitcoin is currently the cryptocurrency with the largest market share. Many previous studies have explored the security of Bitcoin from the perspective of blockchain mining. Especially on the double-spending attacks (DSA), some state-of-the-art studies have proposed various analytical models, aiming to understand the insights behind the double-spen...
Article
Mobile devices can generate a tremendous amount of unique data, and thus create countless opportunities for deep learning tasks. Due to the concerns of data privacy, it is often impractical to log all the data to a central server for training a satisfactory model. Federated learning, in which the participating devices can train a shared global mode...
Article
Full-text available
Nowadays, collaborative filtering recommender systems have been widely deployed in many commercial companies to make profit. Neighborhood‐based collaborative filtering (CF) is common and effective. To date, despite its effectiveness, there has been little effort to explore their robustness and the impact of data poisoning attacks on their performan...
Article
As one of the most important and famous applications of blockchain technology, cryptocurrency has attracted extensive attention recently. Empowered by blockchain technology, all the transaction records of cryptocurrencies are irreversible and recorded in blocks. These transaction records containing rich information and complete traces of financial...
Article
Recent studies have shown that graph neural networks (GNNs) are vulnerable against perturbations due to lack of robustness and can therefore be easily fooled. Currently, most works on attacking GNNs are mainly using gradient information to guide the attack and achieve outstanding performance. However, the high complexity of time and space makes the...
Preprint
Full-text available
Graph data are ubiquitous in the real world. Graph learning (GL) tries to mine and analyze graph data so that valuable information can be discovered. Existing GL methods are designed for centralized scenarios. However, in practical scenarios, graph data are usually distributed in different organizations, i.e., the curse of isolated data islands. To...
Article
Mobile edge computing (MEC) is a promising solution to support resource-constrained devices by offloading tasks to the edge servers. However, traditional approaches (e.g., linear programming and game-theory methods) for computation offloading mainly focus on the immediate performance, potentially leading to performance degradation in the long run....
Article
Blockchain-based cryptocurrencies and applications have flourished in blockchain research community. Massive data generated from diverse blockchain systems bring not only huge business values but also technological challenges in data analytics of heterogeneous blockchain data. Different from Bitcoin and Ethereum, EOSIO has richer diversity and a hi...
Conference Paper
The proliferation of Web services makes it difficult for users to select the most appropriate one among numerous functionally identical or similar service candidates. Quality-of-Service (QoS) describes the non-functional characteristics of Web services, and it has become the key differentiator for service selection. However, users cannot invoke all...
Article
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in communication, computing, caching and control (4Cs) problems. The recent advances in deep reinforcement learning (DRL) algorithms can potentially address the above problems of IoT systems. In this context, this paper provides a comprehensive survey that over...
Article
Full-text available
Decentralized Autonomous Organization (DAO) is believed to play a significant role in our future society governed in a decentralized way. In this article, we first explain the definitions and preliminaries of DAO. Then, we conduct a literature review of the existing studies of DAO published in the recent few years. Through the literature review, we...
Chapter
Graph Neural Networks (GNNs) have shown to be vulnerable against adversarial examples in many works, which encourages researchers to drop substantial attention to its robustness and security. However, so far, the reasons for the success of adversarial attacks and the intrinsic vulnerability of GNNs still remain unclear. The work presented here outl...
Article
Full-text available
Services computing can offer a high-level abstraction to support diverse applications via encapsulating various computing infrastructures. Though services computing has greatly boosted the productivity of developers, it is faced with three main challenges: privacy and security risks, information silo, and pricing mechanisms and incentives. The rece...
Article
Image clustering is a crucial but challenging task in machine learning and computer vision. Its performance highly depends on the quality of image feature representations. Recently, deep joint clustering which combines representation learning with clustering has presented a promising performance. However, existing joint methods suffer from two seve...
Article
Full-text available
Change impact analysis (CIA) is a specialized process of program comprehension that investigates the ripple effects of a code change in a software system. In this paper, we present a boosting way for change impact analysis via mapping the historical change-patterns to current CIA task in a cross-project scenario. The change-patterns reflect the cou...
Research Proposal
Full-text available
CFP: ACM TOSEM Special Section on Security and SE========= We invite contributions for a continuous special section on Security and Software Engineering in ACM Transactions on Software Engineering and Methodology (TOSEM). Software systems are flexible and vulnerable. This leaves the possibility of exploiting such vulnerabilities to the detriment o...
Preprint
Full-text available
Graph Neural Networks (GNNs) have recently shown to be powerful tools for representing and analyzing graph data. So far GNNs is becoming an increasingly critical role in software engineering including program analysis, type inference, and code representation. In this paper, we introduce GraphGallery, a platform for fast benchmarking and easy develo...
Research Proposal
Full-text available
***** Call For Papers ***** 2021 International Conference on Blockchain and Trustworthy Systems (BlockSys'2021) --------------------------------------------------------- Aug. 5-6, 2021, China. http://blocksys.info/2021/ --------------------------------------------------------- Blockchain has become a hot research area in academia and industry. T...
Chapter
In this chapter, we present an overview on how smart contracts could be optimized by intelligence-driven approaches. We empirically study the repetitiveness of smart contracts via cluster analysis and try to extract differentiated codes from the similar contracts. Differentiated codes are defined as the source codes except the repeated ones in two...