Can Wang

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Can Wang (25)
Longbing Cao (6)
Zhong She (5)
Weimin Li (4)
Chihung Chi (3)
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Sen Wang (3)
Weibin Zhao (3)
Yonghong Yu (3)
Yunwei Zhao (3)
Alan Wee Chung Liew (2)
Jiajun Chen (2)
Jianjia Wang (2)
Kwok Yan Lam (2)
Lei Bai (2)
Lin Shang (2)
Mingchun Wang (2)
Qun Jin (2)
Xianzhi Wang (2)
Xiaofeng Zhu (2)

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Knowledge-Based Systems (4)
Complexity (2)
World Wide Web (2)
ACM Transactions on Internet Technology (1)
ACM Transactions on Knowledge Discovery from Data (1)
Applied Soft Computing (1)
Computers and Electrical Engineering (1)
IEEE Transactions on Multimedia (1)
Information Processing and Management: an International Journal (1)

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AAAI'12: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (1)
CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management (1)
Database Systems for Advanced Applications (1)
ICDE '13: Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013) (1)
IJCAI '13: Proceedings of the Twenty-Third international joint conference on Artificial Intelligence (1)
IJCAI'18: Proceedings of the 27th International Joint Conference on Artificial Intelligence (1)
IJCAI'18: Proceedings of the 27th International Joint Conference on Artificial Intelligence (1)
KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (1)
NIPS '20: Proceedings of the 34th International Conference on Neural Information Processing Systems (1)
PAKDD'12: Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II (1)
WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (1)

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Association for Computing Machinery (5)
Elsevier Science Publishers B. V. (5)
AAAI Press (4)
John Wiley & Sons, Inc. (2)
Kluwer Academic Publishers (2)
Pergamon Press, Inc. (2)
Springer-Verlag (2)
Curran Associates Inc. (1)
IEEE Computer Society (1)
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research-article
Heterogeneous network influence maximization algorithm based on multi-scale propagation strength and repulsive force of propagation field
- Chang Guo
  School of Computer Engineering and Science, Shanghai University, Shanghai, China
  ,
- Weimin Li
  School of Computer Engineering and Science, Shanghai University, Shanghai, China
  ,
- Jingchao Wang
  School of Computer Engineering and Science, Shanghai University, Shanghai, China
  ,
- Xiao Yu
  School of Computer Engineering and Science, Shanghai University, Shanghai, China
  ,
- Xiao Liu
  School of Computer Engineering and Science, Shanghai University, Shanghai, China
  ,
- Alex Munyole Luvembe
  School of Computer Engineering and Science, Shanghai University, Shanghai, China
  ,
- Can Wang
  School of Information and Communication Technology, Griffith University, Australia
  ,
- Qun Jin
  Networked Information System Laboratory, Waseda University, Tokyo, Japan
Knowledge-Based Systems, Volume 291, Issue C•May 2024 • https://doi.org/10.1016/j.knosys.2024.111580
Abstract
Heterogeneous networks, like social and academic networks are widespread in the real world, characterized by diverse nodes and complex relationships. Influence maximization is a crucial research topic, in these networks, as it can help in ...
Highlights

Propose a propagation field for solving the influence maximization problem.
Takes advantage of the heterogeneous network’s different structural scales.
GNN is trained for link prediction to mine the global propagation range of ...
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research-article
Interdependence analysis on heterogeneous data via behavior interior dimensions
- Can Wang
  School of Information and Communication Technology, Griffith University, Australia
  ,
- Chi-Hung Chi
  Nanyang Technological University, Singapore
  ,
- Lina Yao
  Data61, CSIRO, Australia
  ,
- Alan Wee-Chung Liew
  School of Information and Communication Technology, Griffith University, Australia
  ,
- Hong Shen
  Faculty of Applied Sciences, Macao Polytechnic University, China
Knowledge-Based Systems, Volume 279, Issue C•Nov 2023 • https://doi.org/10.1016/j.knosys.2023.110893
Abstract
Interdependent dimensions including categorical and continuous variables can be seen commonly as heterogeneous behavioral data in the real world. Mixed-type objects are more or less associated in terms of certain coupling relationships. The usual ...
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Coevolution modeling of group behavior and opinion based on public opinion perception
- Weimin Li
  School of Computer Engineering and Science, Shanghai University, Shanghai, China
  ,
- Chang Guo
  School of Computer Engineering and Science, Shanghai University, Shanghai, China
  ,
- Zhibin Deng
  School of Computer Engineering and Science, Shanghai University, Shanghai, China
  ,
- Fangfang Liu
  School of Computer Engineering and Science, Shanghai University, Shanghai, China
  ,
- Jianjia Wang
  School of Computer Engineering and Science, Shanghai University, Shanghai, China
  ,
- Ruiqiang Guo
  College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, China
  ,
- Can Wang
  School of Information and Communication Technology, Griffith University, Australia
  ,
- Qun Jin
  Networked Information System Laboratory, Waseda University, Tokyo, Japan
Knowledge-Based Systems, Volume 270, Issue C•Jun 2023 • https://doi.org/10.1016/j.knosys.2023.110547
Abstract
It is important to study the evolution mechanism of group behavior for the prevention and control of harmful group behavior on social networks. Most researchers focus on the study of group behavior evolution in a deterministic ...
Highlights

Consider an unstable environment formed by several realistic instability factors.
3Citation
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research-article
Enhancing recommender ensemble by estimating input fitness
- Ye Tao
  Griffith University, 1 Parklands Dr, Southport QLD 4215, Gold Coast, Queensland, Australia
  ,
- Can Wang
  Griffith University, 1 Parklands Dr, Southport QLD 4215, Gold Coast, Queensland, Australia
  ,
- Alan Wee-Chuang Liew
  Griffith University, 1 Parklands Dr, Southport QLD 4215, Gold Coast, Queensland, Australia
  ,
- Sebastian Binnewies
  Griffith University, 1 Parklands Dr, Southport QLD 4215, Gold Coast, Queensland, Australia
Computers and Electrical Engineering, Volume 104, Issue PB•Dec 2022 • https://doi.org/10.1016/j.compeleceng.2022.108442
Abstract
Recommendation system is designed to tackle the information overload problem. The performance of a single recommendation system can be significantly improved if ensemble methods are used. In some recent works researchers seek ways to leverage ...
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We embed both the item sequences and recommenders to estimate the fitness score.
Similar recommenders are close to each other in the embedding space.
Input item sequence is recommended to the best fit recommenders.
Comparing ...
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Collaborative representation learning for nodes and relations via heterogeneous graph neural network
- Weimin Li
  School of Computer Engineering and Science, Shanghai University, Shanghai, China
  ,
- Lin Ni
  School of Computer Engineering and Science, Shanghai University, Shanghai, China
  ,
- Jianjia Wang
  School of Computer Engineering and Science, Shanghai University, Shanghai, China
  ,
- Can Wang
  School of Information and Communication Technology, Griffith University, Australia
Knowledge-Based Systems, Volume 255, Issue C•Nov 2022 • https://doi.org/10.1016/j.knosys.2022.109673
Abstract
Heterogeneous graphs, which consist of multiple types of nodes and edges, are highly suitable for characterizing real-world complex systems. In recent years, due to their strong capability of capturing rich semantics, heterogeneous ...
24Citation
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Published By ACM
Published By ACM
Clustering-based Location Authority Deep Model in the Next Point-of-Interest Recommendation
- Tianxing Wang
  Griffith University, Australia
  ,
- Can Wang
  Griffith University, Australia
  ,
- Hui Tian
  Griffith University, Australia
  ,
- Alan Wee-Chung Liew
  Griffith University, Australia
  ,
- Yunwei Zhao
  CNCERT/CC, China
WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology•December 2021, pp 335-342• https://doi.org/10.1145/3486622.3493943

With the development of location-based social networks (LBSNs), sequential Point-of-Interest (POI) recommendation is getting more and more vitality. Current sequential POI recommendation models predict user’s future mobility based on user’s previous ...
3Citation
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Article
Hyperbolic Personalized Tag Recommendation
- Weibin Zhao
  State Key Lab for Novel Software Technology, Nanjing University, Nanjing, China
  ,
- Aoran Zhang
  Tongda College, Nanjing University of Posts and Telecommunications, Yangzhou, China
  ,
- Lin Shang
  State Key Lab for Novel Software Technology, Nanjing University, Nanjing, China
  ,
- Yonghong Yu
  Tongda College, Nanjing University of Posts and Telecommunications, Yangzhou, China
  ,
- Li Zhang
  Department of Computer Science, Royal Holloway, University of London, Surrey, UK
  ,
- Can Wang
  School of Information and Communication Technology, Griffith University, Brisbane, Australia
  ,
- Jiajun Chen
  State Key Lab for Novel Software Technology, Nanjing University, Nanjing, China
  ,
- Hongzhi Yin
  School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
Database Systems for Advanced Applications•April 2022, pp 216-231• https://doi.org/10.1007/978-3-031-00126-0_14
Abstract
Personalized Tag Recommendation (PTR) aims to automatically generate a list of tags for users to annotate web resources, the so-called items, according to users’ tagging preferences. The main challenge of PTR is to learn representations of ...
2Citation
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An efficient two-factor authentication scheme based on negative databases: Experiments and extensions
- Ran Liu
  School of Computer Science, China University of Geoscience, Wuhan 430078, China
  Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geoscience, Wuhan 430078, China
  ,
- Xiang Wang
  School of Computer Science, China University of Geoscience, Wuhan 430078, China
  ,
- Can Wang
  School of Information and Communication Technology, Griffith University, Gold Coast, Australia
Applied Soft Computing, Volume 119, Issue C•Apr 2022 • https://doi.org/10.1016/j.asoc.2022.108558
Abstract
With the rapid development of network communication technology, identity authentication based on smart cards is one of the most common two-factor authentication schemes. In some real-world applications, timeliness is another challenge ...
1Citation
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An influence maximization method based on crowd emotion under an emotion-based attribute social network
- Weimin Li
  The School of Computer Engineering and Science, Shanghai University, China
  ,
- Yaqiong Li
  The School of Computer Engineering and Science, Shanghai University, China
  ,
- Wei Liu
  The School of Computer Engineering and Science, Shanghai University, China
  ,
- Can Wang
  The School of Information and Communication Technology, Griffith University, Australia
Information Processing and Management: an International Journal, Volume 59, Issue 2•Mar 2022 • https://doi.org/10.1016/j.ipm.2021.102818
Abstract
Most research on influence maximization focuses on the network structure features of the diffusion process but lacks the consideration of multi-dimensional characteristics. This paper proposes the attributed influence maximization based on the ...
30Citation
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Personalized tag recommendation via denoising auto-encoder
- Weibin Zhao
  State Key Lab for Novel Software Technology, Nanjing University, Nanjing, People’s Republic of China
  ,
- Lin Shang
  State Key Lab for Novel Software Technology, Nanjing University, Nanjing, People’s Republic of China
  ,
- Yonghong Yu
  TongDa College, Nanjing University of Posts and Telecommunications, Yangzhou, People’s Republic of China
  ,
- Li Zhang
  Department of Computer Science, Royal Holloway, University of London, Surrey, UK
  ,
- Can Wang
  School of Information and Communication Technology, Griffith University, Queensland, Australia
  ,
- Jiajun Chen
  State Key Lab for Novel Software Technology, Nanjing University, Nanjing, People’s Republic of China
World Wide Web, Volume 26, Issue 1•Jan 2023, pp 95-114 • https://doi.org/10.1007/s11280-021-00967-3
Abstract
Personalized tag recommender systems automatically recommend users a set of tags used to annotate items according to users’ past tagging information. Learning the representations of involved entities (i.e. users, items and tags) and capturing the ...
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Published By ACM
Published By ACM
Sentence Semantic Matching Based on 3D CNN for Human–Robot Language Interaction
- Wenpeng Lu
  School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
  ,
- Rui Yu
  School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
  ,
- Shoujin Wang
  Department of Computing, Macquarie University, Sydney, Australia
  ,
- Can Wang
  School of Information and Communication Technology, Griffith University, Gold Coast, Australia
  ,
- Ping Jian
  School of Computer Science and Technology, Beijing Institute of Technology, Zhongguancun, Beijing, China
  ,
- Heyan Huang
  School of Computer Science and Technology, Beijing Institute of Technology, Zhongguancun, Beijing, China
ACM Transactions on Internet Technology, Volume 21, Issue 4•November 2021, Article No.: 98, pp 1-24 • https://doi.org/10.1145/3450520
The development of cognitive robotics brings an attractive scenario where humans and robots cooperate to accomplish specific tasks. To facilitate this scenario, cognitive robots are expected to have the ability to interact with humans with natural ...
13Citation
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Adaptive graph convolutional recurrent network for traffic forecasting
- Lei Bai
  UNSW, Sydney
  ,
- Lina Yao
  UNSW, Sydney
  ,
- Can Li
  UNSW, Sydney
  ,
- Xianzhi Wang
  University of Technology Sydney
  ,
- Can Wang
  Griffith University
NIPS '20: Proceedings of the 34th International Conference on Neural Information Processing Systems•December 2020, Article No.: 1494, pp 17804-17815

Modeling complex spatial and temporal correlations in the correlated time series data is indispensable for understanding the traffic dynamics and predicting the future status of an evolving traffic system. Recent works focus on designing complicated ...
5Citation
474
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Published By ACM
Spectrum-Guided Adversarial Disparity Learning
- Zhe Liu
  University of New South Wales, Sydney, NSW, Australia
  ,
- Lina Yao
  University of New South Wales, Sydney, NSW, Australia
  ,
- Lei Bai
  University of New South Wales, Sydney, NSW, Australia
  ,
- Xianzhi Wang
  University of Technology Sydney, Sydney, NSW, Australia
  ,
- Can Wang
  Griffith University, Sydney, NSW, Australia
KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining•August 2020, pp 114-124• https://doi.org/10.1145/3394486.3403054

It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between subject-specific variation for each activity class. In this work, we ...
6Citation
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Low-rank hypergraph feature selection for multi-output regression
- Xiaofeng Zhu
  Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, People's Republic of China 541004
  ,
- Rongyao Hu
  Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, People's Republic of China 541004
  ,
- Cong Lei
  Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, People's Republic of China 541004
  ,
- Kim Han Thung
  BRIC Center of the University of North Carolina at Chapel Hill, Chapel Hill, USA 27599
  ,
- Wei Zheng
  Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, People's Republic of China 541004
  ,
- Can Wang
  School of Information and Communication Technology, Griffith University, Queensland, Australia QLD 4222
World Wide Web, Volume 22, Issue 2•March 2019, pp 517-531 • https://doi.org/10.1007/s11280-017-0514-5

Current multi-output regression method usually ignores the relationship among response variables, and thus it is challenging to obtain an effective coefficient matrix for predicting the response variables with the features. We address these problems by ...
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Neural Personalized Ranking via Poisson Factor Model for Item Recommendation
- Rongqing Zhang,
- Can Wang
  School of Information and Communication Technology Griffith University Australia griffith.edu.au
  ,
- Jing Jiang
  College of Tongda Nanjing University of Posts and Telecommunications China njupt.edu.cn
  ,
- Yonghong Yu
  College of Tongda Nanjing University of Posts and Telecommunications China njupt.edu.cn
  ,
- Li Zhang
  Department of Computer and Information Sciences Northumbria University UK northumbria.ac.uk
  ,
- Rong Gao
  School of Computer Science Hubei University of Technology China hbut.edu.cn
  ,
- Weibin Zhao
  College of Tongda Nanjing University of Posts and Telecommunications China njupt.edu.cn
Complexity, Volume 2019•2019 • https://doi.org/10.1155/2019/3563674

Recommender systems have become indispensable for online services since they alleviate the information overload problem for users. Some work has been proposed to support the personalized recommendation by utilizing collaborative filtering to learn the ...
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Published By ACM
Coupled Clustering Ensemble by Exploring Data Interdependence
- Can Wang
  Griffith University, Gold Coast, QLD, Australia
  ,
- Chi-Hung Chi
  Commonwealth Scientific and Industrial Research Organisation, Sandy Bay, TAS,Australia
  ,
- Zhong She
  Griffith University, Gold Coast, QLD, Australia
  ,
- Longbing Cao
  University of Technology Sydney, Sydney, NSW, Australia
  ,
- Bela Stantic
  Griffith University, Gold Coast, QLD, Australia
ACM Transactions on Knowledge Discovery from Data, Volume 12, Issue 6•December 2018, Article No.: 63, pp 1-38 • https://doi.org/10.1145/3230967

Clustering ensembles combine multiple partitions of data into a single clustering solution. It is an effective technique for improving the quality of clustering results. Current clustering ensemble algorithms are usually built on the pairwise agreements ...
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Multi-modality sensor data classification with selective attention
- Xiang Zhang
  School of Computer Science and Engineering, University of New South Wales
  ,
- Lina Yao
  School of Computer Science and Engineering, University of New South Wales
  ,
- Chaoran Huang
  School of Computer Science and Engineering, University of New South Wales
  ,
- Sen Wang
  School of Information and Communication Technology, Griffith University
  ,
- Mingkui Tan
  School of Software Engineering, South China University of Technology
  ,
- Guodong Long
  Center for Quantum Computation and Intelligent Systems, University of Technology Sydney
  ,
- Can Wang
  School of Information and Communication Technology, Griffith University
IJCAI'18: Proceedings of the 27th International Joint Conference on Artificial Intelligence•July 2018, pp 3111-3117

Multimodal wearable sensor data classification plays an important role in ubiquitous computing and has a wide range of applications in scenarios from healthcare to entertainment. However, most existing work in this field employs domain-specific ...
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A comparative study of transactional and semantic approaches for predicting cascades on Twitter
- Yunwei Zhao
  The National Computer Network Emergency Response Technical Team and Coordination Center of China
  ,
- Can Wang
  School of Information and Communication Technology, Griffith University, Australia
  ,
- Chi-Hung Chi
  Data 61, CSIRO, Australia
  ,
- Kwok-Yan Lam
  School of Computer Science and Engineering, Nanyang Technological University, Singapore
  ,
- Sen Wang
  School of Information and Communication Technology, Griffith University, Australia
IJCAI'18: Proceedings of the 27th International Joint Conference on Artificial Intelligence•July 2018, pp 1212-1218

The availability of massive social media data has enabled the prediction of people's future behavioral trends at an unprecedented large scale. Information cascades study on Twitter has been an integral part of behavior analysis. A number of methods based ...
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Behavior-Interior-Aware User Preference Analysis Based on Social Networks
- Xiuzhen Zhang,
- Sen Wang
  Griffith University Australiagriffith.edu.au
  ,
- Kwok-Yan Lam
  Nanyang Technological University Singaporentu.edu.sg
  ,
- Chi-Hung Chi
  CSIRO Australiacsiro.au
  ,
- Can Wang
  Griffith University Australiagriffith.edu.au
  ,
- Min Shu
  CN-CERT Chinacert.org.cn
  ,
- Tao Bo
  Beijing Earthquake Agency China
  ,
- Yun Wei Zhao
  CN-CERT Chinacert.org.cn
Complexity, Volume 2018•2018 • https://doi.org/10.1155/2018/7371209

There is a growing trend recently in big data analysis that focuses on behavior interiors, which concern the semantic meanings (e.g., sentiment, controversy, and other state-dependent factors) in explaining the human behaviors from psychology, sociology, ...
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Graph PCA Hashing for Similarity Search
- Xiaofeng Zhu
  Guangxi Key Laboratory of MIMS and the College of Computer Science and Information Technology, Guangxi Normal University, Guilin, China
  ,
- Xuelong Li
  Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China
  ,
- Shichao Zhang
  Guangxi Key Laboratory of MIMS and the College of Computer Science and Information Technology, Guangxi Normal University, Guilin, China
  ,
- Zongben Xu
  School of Mathematics and Statistics, Xian Jiaotong University, Xian, China
  ,
- Litao Yu
  Queensland Brain Institute, University of Queensland, Queensland, QLD, Australia
  ,
- Can Wang
  School of Information and Communication Technology, Griffith University, Southport, QLD, Australia
IEEE Transactions on Multimedia, Volume 19, Issue 9•Sept. 2017, pp 2033-2044 • https://doi.org/10.1109/TMM.2017.2703636
This paper proposes a new hashing framework to conduct similarity search via the following steps: first, employing linear clustering methods to obtain a set of representative data points and a set of landmarks of the big dataset; second, using the ...
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It is ACM's intention to make the derivation of any publication statistics it generates clear to the user.

Average citations per article = The total Citation Count divided by the total Publication Count.
Citation Count = cumulative total number of times all authored works by this author were cited by other works within ACM's bibliographic database. Almost all reference lists in articles published by ACM have been captured. References lists from other publishers are less well-represented in the database. Unresolved references are not included in the Citation Count. The Citation Count is citations TO any type of work, but the references counted are only FROM journal and proceedings articles. Reference lists from books, dissertations, and technical reports have not generally been captured in the database. (Citation Counts for individual works are displayed with the individual record listed on the Author Page.)
Publication Count = all works of any genre within the universe of ACM's bibliographic database of computing literature of which this person was an author. Works where the person has role as editor, advisor, chair, etc. are listed on the page but are not part of the Publication Count.
Publication Years = the span from the earliest year of publication on a work by this author to the most recent year of publication of a work by this author captured within the ACM bibliographic database of computing literature (The ACM Guide to Computing Literature, also known as "the Guide".
Available for download = the total number of works by this author whose full texts may be downloaded from an ACM full-text article server. Downloads from external full-text sources linked to from within the ACM bibliographic space are not counted as 'available for download'.
Average downloads per article = The total number of cumulative downloads divided by the number of articles (including multimedia objects) available for download from ACM's servers.
Downloads (cumulative) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server since the downloads were first counted in May 2003. The counts displayed are updated monthly and are therefore 0-31 days behind the current date. Robotic activity is scrubbed from the download statistics.
Downloads (12 months) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 12-month period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (12-month download counts for individual works are displayed with the individual record.)
Downloads (6 weeks) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 6-week period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (6-week download counts for individual works are displayed with the individual record.)

ACM Author-Izer Service

Summary Description

ACM Author-Izer is a unique service that enables ACM authors to generate and post links on both their homepage and institutional repository for visitors to download the definitive version of their articles from the ACM Digital Library at no charge.

Downloads from these sites are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to definitive version of ACM articles should reduce user confusion over article versioning.

ACM Author-Izer also extends ACM’s reputation as an innovative “Green Path” publisher, making ACM one of the first publishers of scholarly works to offer this model to its authors.

To access ACM Author-Izer, authors need to establish a free ACM web account. Should authors change institutions or sites, they can utilize the new ACM service to disable old links and re-authorize new links for free downloads from a different site.

How ACM Author-Izer Works

Authors may post ACM Author-Izer links in their own bibliographies maintained on their website and their own institution’s repository. The links take visitors to your page directly to the definitive version of individual articles inside the ACM Digital Library to download these articles for free.

The Service can be applied to all the articles you have ever published with ACM.

Depending on your previous activities within the ACM DL, you may need to take up to three steps to use ACM Author-Izer.

For authors who do not have a free ACM Web Account:

Go to the ACM DL http://dl.acm.org/ and click SIGN UP. Once your account is established, proceed to next step.

For authors who have an ACM web account, but have not edited their ACM Author Profile page:

Sign in to your ACM web account and go to your Author Profile page. Click "Add personal information" and add photograph, homepage address, etc. Click ADD AUTHOR INFORMATION to submit change. Once you receive email notification that your changes were accepted, you may utilize ACM Author-izer.

For authors who have an account and have already edited their Profile Page:

Sign in to your ACM web account, go to your Author Profile page in the Digital Library, look for the ACM Author-izer link below each ACM published article, and begin the authorization process. If you have published many ACM articles, you may find a batch Authorization process useful. It is labeled: "Export as: ACM Author-Izer Service"

ACM Author-Izer also provides code snippets for authors to display download and citation statistics for each “authorized” article on their personal pages. Downloads from these pages are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to the definitive version of ACM articles should reduce user confusion over article versioning.

Note: You still retain the right to post your author-prepared preprint versions on your home pages and in your institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library. But any download of your preprint versions will not be counted in ACM usage statistics. If you use these AUTHOR-IZER links instead, usage by visitors to your page will be recorded in the ACM Digital Library and displayed on your page.

FAQ

Q. What is ACM Author-Izer?
A. ACM Author-Izer is a unique, link-based, self-archiving service that enables ACM authors to generate and post links on either their home page or institutional repository for visitors to download the definitive version of their articles for free.
Q. What articles are eligible for ACM Author-Izer?
A. ACM Author-Izer can be applied to all the articles authors have ever published with ACM. It is also available to authors who will have articles published in ACM publications in the future.

Q. Are there any restrictions on authors to use this service?
A. No. An author does not need to subscribe to the ACM Digital Library nor even be a member of ACM.

Q. What are the requirements to use this service?
A. To access ACM Author-Izer, authors need to have a free ACM web account, must have an ACM Author Profile page in the Digital Library, and must take ownership of their Author Profile page.

Q. What is an ACM Author Profile Page?
A. The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM Digital Library. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community. Please visit the ACM Author Profile documentation page for more background information on these pages.

Q. How do I find my Author Profile page and take ownership?
A. You will need to take the following steps:
- Create a free ACM Web Account
- Sign-In to the ACM Digital Library
- Find your Author Profile Page by searching the ACM Digital Library for your name
- Find the result you authored (where your author name is a clickable link)
- Click on your name to go to the Author Profile Page
- Click the "Add Personal Information" link on the Author Profile Page
- Wait for ACM review and approval; generally less than 24 hours

Q. Why does my photo not appear?
A. Make sure that the image you submit is in .jpg or .gif format and that the file name does not contain special characters

Q. What if I cannot find the Add Personal Information function on my author page?
A. The ACM account linked to your profile page is different than the one you are logged into. Please logout and login to the account associated with your Author Profile Page.

Q. What happens if an author changes the location of his bibliography or moves to a new institution?
A. Should authors change institutions or sites, they can utilize ACM Author-Izer to disable old links and re-authorize new links for free downloads from a new location.

Q. What happens if an author provides a URL that redirects to the author’s personal bibliography page?
A. The service will not provide a free download from the ACM Digital Library. Instead the person who uses that link will simply go to the Citation Page for that article in the ACM Digital Library where the article may be accessed under the usual subscription rules.

However, if the author provides the target page URL, any link that redirects to that target page will enable a free download from the Service.

Q. What happens if the author’s bibliography lives on a page with several aliases?
A. Only one alias will work, whichever one is registered as the page containing the author’s bibliography. ACM has no technical solution to this problem at this time.

Q. Why should authors use ACM Author-Izer?
A. ACM Author-Izer lets visitors to authors’ personal home pages download articles for no charge from the ACM Digital Library. It allows authors to dynamically display real-time download and citation statistics for each “authorized” article on their personal site.

Q. Does ACM Author-Izer provide benefits for authors?
A. Downloads of definitive articles via Author-Izer links on the authors’ personal web page are captured in official ACM statistics to more accurately reflect usage and impact measurements.

Authors who do not use ACM Author-Izer links will not have downloads from their local, personal bibliographies counted. They do, however, retain the existing right to post author-prepared preprint versions on their home pages or institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library.

Q. How does ACM Author-Izer benefit the computing community?
A. ACM Author-Izer expands the visibility and dissemination of the definitive version of ACM articles. It is based on ACM’s strong belief that the computing community should have the widest possible access to the definitive versions of scholarly literature. By linking authors’ personal bibliography with the ACM Digital Library, user confusion over article versioning should be reduced over time.

In making ACM Author-Izer a free service to both authors and visitors to their websites, ACM is emphasizing its continuing commitment to the interests of its authors and to the computing community in ways that are consistent with its existing subscription-based access model.

Q. How do authors access ACM Author-Izer?
A. See How to Use

Q. Why can’t I find my most recent publication in my ACM Author Profile Page?
A. There is a time delay between publication and the process which associates that publication with an Author Profile Page. Right now, that process usually takes 4-8 weeks.

Q. How does ACM Author-Izer expand ACM’s “Green Path” Access Policies?
A. ACM Author-Izer extends the rights and permissions that authors retain even after copyright transfer to ACM, which has been among the “greenest” publishers. ACM enables its author community to retain a wide range of rights related to copyright and reuse of materials. They include:
- Posting rights that ensure free access to their work outside the ACM Digital Library and print publications
- Rights to reuse any portion of their work in new works that they may create
- Copyright to artistic images in ACM’s graphics-oriented publications that authors may want to exploit in commercial contexts
- All patent rights, which remain with the original owner

Can Wang

Can Wang

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Results

Heterogeneous network influence maximization algorithm based on multi-scale propagation strength and repulsive force of propagation field

Interdependence analysis on heterogeneous data via behavior interior dimensions

Coevolution modeling of group behavior and opinion based on public opinion perception

Enhancing recommender ensemble by estimating input fitness

Collaborative representation learning for nodes and relations via heterogeneous graph neural network

Clustering-based Location Authority Deep Model in the Next Point-of-Interest Recommendation

Hyperbolic Personalized Tag Recommendation

An efficient two-factor authentication scheme based on negative databases: Experiments and extensions

An influence maximization method based on crowd emotion under an emotion-based attribute social network

Personalized tag recommendation via denoising auto-encoder

Sentence Semantic Matching Based on 3D CNN for Human–Robot Language Interaction

Adaptive graph convolutional recurrent network for traffic forecasting

Spectrum-Guided Adversarial Disparity Learning

Low-rank hypergraph feature selection for multi-output regression

Neural Personalized Ranking via Poisson Factor Model for Item Recommendation

Coupled Clustering Ensemble by Exploring Data Interdependence

Multi-modality sensor data classification with selective attention

A comparative study of transactional and semantic approaches for predicting cascades on Twitter

Behavior-Interior-Aware User Preference Analysis Based on Social Networks

Graph PCA Hashing for Similarity Search