Chenyang Bu

Chenyang Bu
Hefei University of Technology · Department of Computer Science & Technology

PhD

About

42
Publications
3,180
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
324
Citations
Introduction
Chenyang Bu obtained the PhD degree from University of Science and Technology of China. He serves as a reviewer for several international journals including 10+ IEEE/ACM Transactions. He is a PC member of AAAI'2023 and the web chair of ICBK'2021 and ICKG’ 2022. He has published 30+ papers in prestigious journals including IEEE TEVC, IEEE TETCI, IEEE TSUSC, and IEEE TKDE. His research interests include evolutionary algorithms and their applications in knowledge graphs and educational data mining.
Additional affiliations
July 2017 - April 2020
Hefei University of Technology
Position
  • Professor (Assistant)
June 2017 - December 2019
Hefei University of Technology
Position
  • PostDoc Position
Description
  • Supervisor: Prof. Xindong Wu.
Education
September 2012 - June 2017
University of Science and Technology of China
Field of study
  • Computer Science
September 2008 - June 2012
Hefei University of Technology
Field of study
  • Computer Science

Publications

Publications (42)
Article
Cognitive diagnosis has attracted increasing attention owing to the flourishing development of online education. As one of the most widely used cognitive diagnostic models, DINA (Deterministic Inputs, Noisy And gate) evaluates students’ knowledge mastery based on their performance of the exercises. However, the traditional DINA model and its varian...
Conference Paper
Investment promotion refers to the process by which the government uses disposable resources to attract investors to the region for production and business activities. The existing basic mode of attracting investment is to collect information about enterprises and entrepreneurs through manual methods, determine the target enterprise from the list o...
Article
Full-text available
Dynamic Constrained Optimization Problems (DCOPs) are difficult to solve because both the objective function and constraints can vary with time. Although DCOPs have drawn attention in recent years, little work has been performed to solve DCOPs with multiple dynamic feasible regions from the perspective of locating and tracking multiple feasible reg...
Article
Considering the uncertainty of power generations, in addition to the uncertainty of loads, is more and more important because of the increasing use of renewable energy sources. Most existing works on dynamic optimal power flow (DOPF) have only focused on either the uncertainty of loads (called demand-side uncertainty) or the uncertainty of power ge...
Conference Paper
In recent years, automated machine learning (AutoML) has received widespread attention from academia and industry owing to its ability to significantly reduce the threshold and labor cost of machine learning. It has demonstrated its powerful functions in hyperparameter optimization, model selection, neural network search, and feature engineering. M...
Conference Paper
Knowledge tracing (KT) is a fundamental task in educational data mining that mainly focuses on students’ dynamic cognitive states of skills. The question-answering process of students can be regarded as a thinking process that considers the following two problems. One problem is which skills are needed to answer the question, and the other is how t...
Conference Paper
Gradient-based repair aims to repair infeasible solutions to feasible ones using the gradient information of the constraints. As an effective constraint handling method, gradient-based repair has received extensive attention and has been applied in various evolutionary algorithms (EAs). Nevertheless, due to the complexity of constraints in practica...
Article
Full-text available
Entity alignment (EA) aims to automatically determine whether an entity pair in different knowledge bases or knowledge graphs refer to the same entity in reality. Inspired by human cognitive mechanisms, we propose a coarse-to-fine entity alignment model (called CFEA) consisting of three stages: coarse-grained, middle-grained, and fine-grained. In t...
Conference Paper
Named entity recognition (NER) is a basic task of natural language processing (NLP), whose purpose is to identify named entities such as the names of persons, places, and organizations in the corpus. Utilizing neural networks for feature extraction, followed by conditional random field (CRF) layer decoding, is effective for the NER task. However, a...
Article
The objective of entity alignment is to judge whether entities refer to the same object in the real world. Methods for entity alignment can be grossly divided into two groups: conventional symbol-based entity alignment methods and embedding-based entity alignment methods. Both groups of methods have advantages and disadvantages (which are detailed...
Article
Graph edge partitioning, which is essential for the efficiency of distributed graph computation systems, divides a graph into several balanced partitions within a given size to minimize the number of vertices to be cut. Existing graph partitioning models can be classified into two categories: offline and streaming graph partitioning models. The for...
Article
The automatic construction of knowledge graphs (KGs) from multiple data sources has received increasing attention. The automatic construction process inevitably brings considerable noise, especially in the construction of KGs from unstructured text. The noise in a KG can be divided into two categories: factual noise and low-quality noise. Factual n...
Conference Paper
Knowledge representation learning is usually used in knowledge reasoning and other related fields. Its goal is to use low-dimensional vectors to represent the entities and relations in a knowledge graph. In the process of automatic knowledge graph construction, the complexity of unstructured text and the incorrect text may make automatic constructi...
Article
Linking ambiguous entity mentions in a text with their true mapping entities in a heterogeneous information network (HIN) is important. Most of existing entity linking methods with HINs assume that the entities in a text are independent while ignoring the relationships between the entities in context. Recent studies have shown that collective entit...
Article
Online education promotes the sharing of learning resources. Knowledge tracing (KT) is aimed at tracking the cognition function of students according to their performance on various exercises at different times and has attracted considerable attention. Existing KT models primarily use bisection representations for the performance and cognitive stat...
Article
Genealogical knowledge graphs depict the relationships of family networks and the development of family histories. They can help researchers to analyze and understand genealogical data, search for genealogical descendant paths, and explore the origins of a family more easily. However, the heterogenous, autonomous, complex, and evolving natures of g...
Article
Full-text available
Evolutionary community discovery is a hot research topic related to the dynamic or temporal social networks. The communities detected in a dynamic network should get reasonable partition for the current network and do not deviate drastically from the previous ones. This paper is an extended version of our previous work in Gao et al. (in: Proceeding...
Article
Full-text available
With the advent of the era of big data, the scale of data has grown dramatically, and there is a close correlation between massive multi-source heterogeneous data, which can be visually depicted by a big graph. Big graph, especially from Web data, social networks, or biometric data, has attracted more and more attention from researchers, which usua...
Article
In evolutionary dynamic optimization (EDO), most of the existing studies have assumed that dynamic optimization problems are black boxes. However, for many real-world problems, the dynamic parameters that cause the problems to change are observable. However, determining the utility of these parameters in improving optimization performance has not y...
Conference Paper
Full-text available
Entity alignment aims to automatically determine whether an entity pair in different knowledge graphs refers to the same entity in reality. Existing entity alignment methods can be classified into two categories: string-similarity-based methods and embedding-based methods. String-similarity-based methods have higher accuracy, however, they might ha...
Chapter
Entity alignment aims to automatically determine whether an entity pair in different knowledge graphs refers to the same entity in reality. Existing entity alignment methods can be classified into two categories: string-similarity-based methods and embedding-based methods. String-similarity-based methods have higher accuracy, however, they might ha...
Preprint
Considering the uncertainty of power generations, in addition to the uncertainty of loads, is more and more important because of the increasing use of renewable energy sources. Most existing works on dynamic optimal power flow (DOPF) have only focused on either the uncertainty of loads (called demand-side uncertainty) or the uncertainty of power ge...
Conference Paper
Graph edge partitioning divides the edges of an input graph into multiple balanced partitions of a given size to minimize the sum of vertices that are cut, which is critical to the performance of distributed graph computation platforms. Existing graph partitioning methods can be classified into two categories: offline graph partitioning and streami...
Conference Paper
Knowledge graph embedding (KGE) can benefit a variety of downstream tasks, such as link prediction and relation extraction, and has therefore quickly gained much attention. However, most conventional embedding models assume that all triple facts share the same confidence without any noise, which is inappropriate. In fact, many noises and conflicts...
Conference Paper
Full-text available
High-Dimensional Dynamic Optimization Problems (HDDOPs) commonly exist in real-world applications. In evolutionary computation field, most of existing benchmark problems, which could simulate HDDOPs, are non-separable. Thus, we give a novel benchmark problem, called high-dimensional moving peaks benchmark to simulate separable, partially separable,...
Article
The increasing demand for knowledge from network data poses significant challenges in many tasks. Discovering community structure from a network is one of the classic and significant problems faced in network analysis. In this paper, we study the network structure from the perspective of the composition of fuzzy relations, and a novel algorithm bas...
Conference Paper
Full-text available
A recently proposed clustering algorithm named Clustering by fast search and Find of Density Peaks (CFDP) can automatically identify the cluster centers without an iterative process. The key step in CFDP is searching for the nearest neighbor with higher density for each point. However, the CFDP algorithm may not be effective for cases in which ther...
Conference Paper
Clustering is a classical unsupervised learning task, which is aimed to divide a data set into several groups with similar objects. Clustering problem has been studied for many years, and many excellent clustering algorithms have been proposed. In this paper, we propose a novel clustering method based on density, which is simple but effective. The...
Article
Dynamic Time-linkage Optimization Problems (DTPs) are a special class of Dynamic Optimization Problems (DOPs) with the feature of time-linkage. Time-linkage means that the decisions taken now could influence the problem states in future. Although DTPs are common in practice, attention from the field of evolutionary optimization is little. To date,...
Conference Paper
Clustering evolutionary data (or called evolutionary clustering) has received an enormous amount of attention in recent years. A recent framework (called temporal smoothness) considers that the clustering result should depend mainly on the current data while simultaneously not deviate too much from previous ones. In this paper, evolutionary data is...
Conference Paper
Evolutionary community discovery is a hot research topic which clusters the dynamic or temporal network. The communities detected in dynamic network should get reasonable partition for the current data while simultaneously not deviate drastically from the previous ones. In this paper, the evolutionary community discovery algorithm based on leader n...
Conference Paper
Full-text available
Evolutionary Algorithms (EAs) with gradient-based repair, which utilize the gradient information of the constraints set, have been proved to be effective. It is known that it would be time-consuming if all infeasible individuals are repaired. Therefore, so far the infeasible individuals to be repaired are randomly selected from the population and t...

Network

Cited By

Projects

Project (1)
Project
As a representative knowledge graph embedding method, the translation-based model (TransE) aims to embed the semantic information of a knowledge base into low-dimensional vector spaces. Because real-world data usually change over time, it is of great significance to study the online updating problem of TransE. However, existing studies mainly focus on static data, and the online updating of TransE has not drawn much attention. This proposal intends to study the online updating of translation-based models from the perspective of dynamic optimization. More specifically, main research contents include: (1) the translation-based models for dynamic data, including the study of optimization functions that do not depend on negative samples to reduce the impact of the expired data on the model; (2) the incremental processing of dynamic data from the perspective of dynamic optimization to avoid re-training the model; (3) the grouping strategy of large-scale optimization problem, so as to decompose the updating problem of translation-based models into several sub-problems to further save the updating time; and (4) constructing a prototype system for dynamic social networks to provide in-depth studies and further improvement of these research contents. This proposal provides a new solution to the updating of the translation-based models, and can contribute to the research on evolutionary dynamic optimization algorithms with practical values.