Sang-wook Kim
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- SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing (6)
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- WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (3)
- WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web (3)
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- short-paperOpen Access
Published By ACM
Published By ACM
Empowering Traffic Speed Prediction with Auxiliary Feature-Aided Dependency Learning
Dong-hyuk Seo
Hanyang University, Seoul, Republic of Korea
,Jiwon Son
Hanyang University, Seoul, Republic of Korea
,Namhyuk Kim
Hyundai Motor Company, Seoul, Republic of Korea
,Won-Yong Shin
Yonsei University, Seoul, Republic of Korea
,Sang-Wook Kim
Hanyang University, Seoul, Republic of Korea
CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management•October 2024, pp 4031-4035• https://doi.org/10.1145/3627673.3679909Traffic speed prediction is a crucial task for optimizing navigation systems and reducing traffic congestion. Although there have been efforts to improve the accuracy of speed prediction by incorporating auxiliary features, such as traffic flow, weather, ...
- 0Citation
- 144
- Downloads
MetricsTotal Citations0Total Downloads144Last 12 Months144Last 6 weeks47
- research-articleOpen Access
Published By ACM
Published By ACM
Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation
Neng Kai Nigel Neo
Georgia Institute of Technology, Atlanta, GA, USA
,Yeon-Chang Lee
Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea
,Yiqiao Jin
Georgia Institute of Technology, Atlanta, GA, USA
,Sang-Wook Kim
Hanyang University, Seoul, Republic of Korea
,Srijan Kumar
Georgia Institute of Technology, Atlanta, GA, USA
CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management•October 2024, pp 1752-1762• https://doi.org/10.1145/3627673.3679754The Fair Graph Anomaly Detection (FairGAD) problem aims to accurately detect anomalous nodes in an input graph while avoiding biased predictions against individuals from sensitive subgroups. However, the current literature does not comprehensively ...
- 0Citation
- 283
- Downloads
MetricsTotal Citations0Total Downloads283Last 12 Months283Last 6 weeks79
- research-articleOpen Access
Published By ACM
Published By ACM
Leveraging Trustworthy Node Attributes for Effective Network Alignment
Dong-Hyuk Seo
Computer Science, Hanyang University, Seoul, Republic of Korea
,Jae-Hwan Lim
Computer Science, Hanyang University, Seoul, Republic of Korea
,Won-Yong Shin
Computational Science and Engineering, Yonsei University, Seoul, Republic of Korea
,Sang-Wook Kim
Computer Science, Hanyang University, Seoul, Republic of Korea
CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management•October 2024, pp 2004-2013• https://doi.org/10.1145/3627673.3679658With the prevalence of social media platforms, accurately identifying the same users across different networks through network alignment has become crucial. Existing methods often struggle due to sparse or absent user-identifiable information (node ...
- 0Citation
- 119
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MetricsTotal Citations0Total Downloads119Last 12 Months119Last 6 weeks38
- research-articleOpen Access
Published By ACM
Published By ACM
PolarDSN: An Inductive Approach to Learning the Evolution of Network Polarization in Dynamic Signed Networks
Min-Jeong Kim
Hanyang University, Seoul, Republic of Korea
,Yeon-Chang Lee
Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea
,Sang-Wook Kim
Hanyang University, Seoul, Republic of Korea
CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management•October 2024, pp 1099-1109• https://doi.org/10.1145/3627673.3679654The goal of dynamic signed network embedding (DSNE) is to represent the nodes in a dynamic signed network (DSN) as embeddings that preserve the evolving nature of conflicting relationships between nodes. While existing DSNE methods are useful for ...
- 0Citation
- 148
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MetricsTotal Citations0Total Downloads148Last 12 Months148Last 6 weeks36
- research-article
Published By ACM
Published By ACM
Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding
Min-Jeong Kim
Hanyang University, Seoul, Korea
,Yeon-Chang Lee
Ulsan National Institute of Science and Technology (UNIST), Ulsan, Korea
,David Y. Kang
Chungbuk National University, Cheongju, Korea
,Sang-Wook Kim
Hanyang University, Seoul, Korea
ACM Transactions on Knowledge Discovery from Data, Volume 18, Issue 9•November 2024, Article No.: 219, pp 1-26 • https://doi.org/10.1145/3685279The problem of representing nodes in a signed network as low-dimensional vectors, known as signed network embedding (SNE), has garnered considerable attention in recent years. While several SNE methods based on graph convolutional networks (GCNs) have ...
- 0Citation
- 246
- Downloads
MetricsTotal Citations0Total Downloads246Last 12 Months246Last 6 weeks74
- surveyOpen Access
Published By ACM
Published By ACM
A Survey of Graph Neural Networks for Social Recommender Systems
Kartik Sharma
Georgia Institute of Technology, Atlanta, United States
,Yeon-Chang Lee
Ulsan National Institute of Science and Technology, Ulsan, Korea (the Republic of)
,Sivagami Nambi
Georgia Institute of Technology, Atlanta, United States
,Aditya Salian
Georgia Institute of Technology, Atlanta, United States
,Shlok Shah
Georgia Institute of Technology, Atlanta, United States
,Sang-Wook Kim
Hanyang University, Seongdong-gu, Korea (the Republic of)
,Srijan Kumar
Georgia Institute of Technology, Atlanta, United States
ACM Computing Surveys, Volume 56, Issue 10•October 2024, Article No.: 265, pp 1-34 • https://doi.org/10.1145/3661821Social recommender systems (SocialRS) simultaneously leverage the user-to-item interactions as well as the user-to-user social relations for the task of generating item recommendations to users. Additionally exploiting social relations is clearly ...
- 17Citation
- 7,198
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MetricsTotal Citations17Total Downloads7,198Last 12 Months7,198Last 6 weeks1,254
- short-paperOpen Access
Published By ACM
Published By ACM
RealGraphGPU++: A High-Performance GPU-Based Graph Engine with Direct Storage-to-DM IO
Jeong-Min Park
Hanyang University, Seoul, Republic of Korea
,Myung-Hwan Jang
Hanyang University, Seoul, Republic of Korea
,Duck-Ho Bae
Samsung Electronics, Hwaseong-si, Republic of Korea
,Sang-Wook Kim
Hanyang Universty, Seoul, Republic of Korea
WWW '24: Companion Proceedings of the ACM Web Conference 2024•May 2024, pp 654-657• https://doi.org/10.1145/3589335.3651549Recently, with the increasing size of real-world networks, graph engines have been studied extensively for efficient graph analysis. As one of the state-of-the-art single-machine-based graph engines, \textRealGraph ^\textGPU processes large-scale graphs ...
- 0Citation
- 258
- Downloads
MetricsTotal Citations0Total Downloads258Last 12 Months258Last 6 weeks52- 1
Supplementary Materialshp3576.mp4
- short-paperOpen Access
Published By ACM
Published By ACM
Item-Ranking Promotion in Recommender Systems
Hong-Kyun Bae
Hanyang University, Seoul, Republic of Korea
,Hae-Ri Jang
Hanyang University, Seoul, Republic of Korea
,Yang-Sae Moon
Kangwon National University, Chuncheon, Republic of Korea
,Sang-Wook Kim
Hanyang University, Seoul, Republic of Korea
WWW '24: Companion Proceedings of the ACM Web Conference 2024•May 2024, pp 505-508• https://doi.org/10.1145/3589335.3651529In this paper, we first define the problem of item-ranking promotion (IRP) in recommender systems as (Goal 1) maintaining a high level of overall recommendation accuracy while (Goal 2) recommending the items with extra values (i.e., RP-items) to as many ...
- 0Citation
- 378
- Downloads
MetricsTotal Citations0Total Downloads378Last 12 Months378Last 6 weeks47- 1
Supplementary Materialshp0497.mp4
- short-paperOpen Access
Published By ACM
Published By ACM
Is the 'Impression Log' Beneficial to Evaluating News Recommender Systems? No, it is Not!
Jeewon Ahn
Hanyang University, Seoul, Republic of Korea
,Hong-Kyun Bae
Hanyang University, Seoul, Republic of Korea
,Sang-Wook Kim
Hanyang University, Seoul, Republic of Korea
WWW '24: Companion Proceedings of the ACM Web Conference 2024•May 2024, pp 822-825• https://doi.org/10.1145/3589335.3651527This paper aims to answer the question of whether to use the impression log in evaluating news recommendation models. We start with a claim that the testing with the impression log composed of only hard-negative news (i.e., impression (IMP)-based test) ...
- 0Citation
- 277
- Downloads
MetricsTotal Citations0Total Downloads277Last 12 Months277Last 6 weeks38- 1
Supplementary Materialshp6550.mp4
- short-paperOpen Access
Published By ACM
Published By ACM
RealGraphGPUWeb: A Convenient and Efficient GPU-Based Graph Analysis Platform on the Web
Jeong-Min Park
Department of Computer Science, Hanyang University, Seoul, Republic of Korea
,Myung-Hwan Jang
Department of Computer Science, Hanyang University, Seoul, Republic of Korea
,Sang-Wook Kim
Department of Computer Science, Hanyang University, Seoul, Republic of Korea
WWW '24: Companion Proceedings of the ACM Web Conference 2024•May 2024, pp 1011-1014• https://doi.org/10.1145/3589335.3651237In this demo paper, we present RealGraphGPUWeb a web-based graph analysis platform with the following features: (1) easy to use user-friendly GUI, (2) high processing performance, (3) various graph algorithms and data formats supported, (4) high ...
- 1Citation
- 167
- Downloads
MetricsTotal Citations1Total Downloads167Last 12 Months167Last 6 weeks20- 1
Supplementary Materialde3328.mp4
- research-articleOpen Access
Published By ACM
Published By ACM
Negative Sampling in Next-POI Recommendations: Observation, Approach, and Evaluation
Hong-Kyun Bae
Hanyang University, Seoul, Republic of Korea
,Yebeen Kim
Hanyang University, Seoul, Republic of Korea
,Hyunjoon Kim
Hanyang University, Seoul, Republic of Korea
,Sang-Wook Kim
Hanyang University, Seoul, Republic of Korea
WWW '24: Proceedings of the ACM Web Conference 2024•May 2024, pp 3888-3899• https://doi.org/10.1145/3589334.3645681To recommend the points of interest (POIs) that a user would check-in next, most deep-learning (DL)-based existing studies have employed random negative (RN) sampling during model training. In this paper, we claim and validate that, as the training ...
- 0Citation
- 493
- Downloads
MetricsTotal Citations0Total Downloads493Last 12 Months493Last 6 weeks62- 1
Supplementary Materialrfp2214.mp4
- research-article
Published By ACM
Published By ACM
Low Mileage, High Fidelity: Evaluating Hypergraph Expansion Methods by Quantifying the Information Loss
David Y. Kang
University of Michigan, Ann Arbor, MI, USA
,Qiaozhu Mei
University of Michigan, Ann Arbor, MI, USA
,Sang-Wook Kim
Hanyang University, Seoul, Republic of Korea
WWW '24: Proceedings of the ACM Web Conference 2024•May 2024, pp 959-968• https://doi.org/10.1145/3589334.3645657In this paper, we first define information loss that occurs in the hypergraph expansion and then propose a novel framework, named MILEAGE, to evaluate hypergraph expansion methods by measuring their degree of information loss. MILEAGE employs the ...
- 0Citation
- 153
- Downloads
MetricsTotal Citations0Total Downloads153Last 12 Months153Last 6 weeks19
- research-articleOpen Access
Published By ACM
Published By ACM
HearHere: Mitigating Echo Chambers in News Consumption through an AI-based Web System
Youngseung Jeon
University of California Los Angeles, Los Angeles, CA, USA
,Jaehoon Kim
Hanyang University, Seoul, Republic of Korea
,Sohyun Park
Ajou University, Suwon, Republic of Korea
,Yunyong Ko
Chung-Ang University, Seoul, Republic of Korea
,Seongeun Ryu
Hanyang University, Seoul, Republic of Korea
,Sang-Wook Kim
Hanyang University, Seoul, Republic of Korea
,Kyungsik Han
Hanyang University, Seoul, Republic of Korea
Proceedings of the ACM on Human-Computer Interaction, Volume 8, Issue CSCW1•April 2024, Article No.: 63, pp 1-34 • https://doi.org/10.1145/3637340Considerable efforts are currently underway to mitigate the negative impacts of echo chambers, such as increased susceptibility to fake news and resistance towards accepting scientific evidence. Prior research has presented the development of computer ...
- 2Citation
- 460
- Downloads
MetricsTotal Citations2Total Downloads460Last 12 Months460Last 6 weeks72
- research-article
Published By ACM
Published By ACM
MONET: Modality-Embracing Graph Convolutional Network and Target-Aware Attention for Multimedia Recommendation
Yungi Kim
Hanyang University, Seoul, Republic of Korea
,Taeri Kim
Hanyang University, Seoul, Republic of Korea
,Won-Yong Shin
Yonsei University, Seoul, Republic of Korea
,Sang-Wook Kim
Hanyang University, Seoul, Republic of Korea
WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining•March 2024, pp 332-340• https://doi.org/10.1145/3616855.3635817In this paper, we focus on multimedia recommender systems using graph convolutional networks (GCNs) where the multimodal features as well as user-item interactions are employed together. Our study aims to exploit multimodal features more effectively in ...
- 3Citation
- 343
- Downloads
MetricsTotal Citations3Total Downloads343Last 12 Months343Last 6 weeks30
- research-article
VITA: 'carefully chosen and weighted less' is better in medication recommendation
Taeri Kim
Department of Computer Science, Hanyang University, South Korea
,Jiho Heo
Department of Computer Science, Hanyang University, South Korea
,Hongil Kim
Department of Artificial Intelligence, Hanyang University, South Korea
,Kijung Shin
Kim Jaechul Graduate School of AI & School of Electrical Engineering, KAIST, South Korea
,Sang-Wook Kim
Department of Computer Science, Hanyang University, South Korea
AAAI'24/IAAI'24/EAAI'24: Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence•February 2024, Article No.: 956, pp 8600-8607• https://doi.org/10.1609/aaai.v38i8.28704We address the medication recommendation problem, which aims to recommend effective medications for a patient's current visit by utilizing information (e.g., diagnoses and procedures) given at the patient's current and past visits. While there exist a ...
- 0Citation
MetricsTotal Citations0
- research-article
Learning to compensate for lack of information: Extracting latent knowledge for effective temporal knowledge graph completion
Yeon-Chang Lee
Georgia Institute of Technology, Atlanta, GA, USA
,JaeHyun Lee
Hanyang University, Seoul, Korea
,Dongwon Lee
The Pennsylvania State University, University Park, PA, USA
,Sang-Wook Kim
Hanyang University, Seoul, Korea
Information Sciences: an International Journal, Volume 654, Issue C•Jan 2024 • https://doi.org/10.1016/j.ins.2023.119857AbstractThe goal of temporal knowledge graph embedding (TKGE) is to represent the entities and relations in a given temporal knowledge graph (TKG) as low-dimensional vectors (i.e., embeddings), which preserve both semantic information and temporal ...
- 1Citation
MetricsTotal Citations1
- research-article
A Framework for Accurate Community Detection on Signed Networks Using Adversarial Learning
David Y. Kang
School of Information, University of Michigan, Ann Arbor, MI, USA
,Woncheol Lee
Department of Computer Science, Hanyang University, Seoul, South Korea
,Yeon-Chang Lee
School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
,Kyungsik Han
Department of Intelligence Computing, Hanyang University, Seoul, South Korea
,Sang-Wook Kim
Department of Computer Science, Hanyang University, Seoul, South Korea
IEEE Transactions on Knowledge and Data Engineering, Volume 35, Issue 11•Nov. 2023, pp 10937-10951 • https://doi.org/10.1109/TKDE.2022.3231104In this article, we propose a framework for embedding-based community detection on signed networks, namely <underline><italic>A</italic></underline>dversarial learning of <underline><italic>B</italic></underline>alanced triangle for <underline><italic>C</...
- 0Citation
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- research-article
Published By ACM
Published By ACM
SAGE: A Storage-Based Approach for Scalable and Efficient Sparse Generalized Matrix-Matrix Multiplication
Myung-Hwan Jang
Hanyang University, Seoul, Republic of Korea
,Yunyong Ko
Hanyang University, Seoul, Republic of Korea
,Hyuck-Moo Gwon
Hanyang University, Seoul, Republic of Korea
,Ikhyeon Jo
Hanyang University, Seoul, Republic of Korea
,Yongjun Park
Yonsei University, Seoul, Republic of Korea
,Sang-Wook Kim
Hanyang University, Seoul, Republic of Korea
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management•October 2023, pp 923-933• https://doi.org/10.1145/3583780.3615044Sparse generalized matrix-matrix multiplication (SpGEMM) is a fundamental operation for real-world network analysis. With the increasing size of real-world networks, the single-machine-based SpGEMM approach cannot perform SpGEMM on large-scale networks, ...
- 0Citation
- 145
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MetricsTotal Citations0Total Downloads145Last 12 Months62Last 6 weeks5
- research-article
Published By ACM
Published By ACM
ELTRA: An Embedding Method based on Learning-to-Rank to Preserve Asymmetric Information in Directed Graphs
Masoud Rehyani Hamedani
Hanyang University, Seoul, Republic of Korea
,Jin-Su Ryu
Hanyang University, Seoul, Republic of Korea
,Sang-Wook Kim
Hanyang University, Seoul, Republic of Korea
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management•October 2023, pp 2116-2125• https://doi.org/10.1145/3583780.3614862Double-vector embedding methods capture the asymmetric information in directed graphs first, and then preserve them in the embedding space by providingtwo latent vectors, i.e., source and target, per node. Although these methods are known to besuperior ...
- 0Citation
- 122
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MetricsTotal Citations0Total Downloads122Last 12 Months65Last 6 weeks2
- research-articlefree
Published By ACM
Published By ACM
LATTE: A Framework for Learning Item-Features to Make a Domain-Expert for Effective Conversational Recommendation
Taeho Kim
Hanyang University, Seoul, South Korea
,Juwon Yu
Hanyang University, Seoul, South Korea
,Won-Yong Shin
Yonsei University, Seoul, South Korea
,Hyunyoung Lee
KT Corporation, Seoul, South Korea
,Ji-hui Im
KT Corporation, Seoul, South Korea
,Sang-Wook Kim
Hanyang University, Seoul, South Korea
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining•August 2023, pp 1144-1153• https://doi.org/10.1145/3580305.3599401For high-quality conversational recommender systems (CRS), it is important to recommend the suitable items by capturing the items' features mentioned in the dialog and to explain the appropriate ones among the various features of the recommended item. ...
- 6Citation
- 1,003
- Downloads
MetricsTotal Citations6Total Downloads1,003Last 12 Months500Last 6 weeks21- 1
Supplementary Materialrtfp0123-2min-promo.mp4
Author Profile Pages
- Description: The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM bibliographic database, the Guide. Coverage of ACM publications is comprehensive from the 1950's. Coverage of other publishers generally starts in the mid 1980's. 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 see the following 2007 Turing Award winners' profiles as examples: - History: Disambiguation of author names is of course required for precise identification of all the works, and only those works, by a unique individual. Of equal importance to ACM, author name normalization is also one critical prerequisite to building accurate citation and download statistics. For the past several years, ACM has worked to normalize author names, expand reference capture, and gather detailed usage statistics, all intended to provide the community with a robust set of publication metrics. The Author Profile Pages reveal the first result of these efforts.
- Normalization: ACM uses normalization algorithms to weigh several types of evidence for merging and splitting names.
These include:- co-authors: if we have two names and cannot disambiguate them based on name alone, then we see if they have a co-author in common. If so, this weighs towards the two names being the same person.
- affiliations: names in common with same affiliation weighs toward the two names being the same person.
- publication title: names in common whose works are published in same journal weighs toward the two names being the same person.
- keywords: names in common whose works address the same subject matter as determined from title and keywords, weigh toward being the same person.
The more conservative the merging algorithms, the more bits of evidence are required before a merge is made, resulting in greater precision but lower recall of works for a given Author Profile. Many bibliographic records have only author initials. Many names lack affiliations. With very common family names, typical in Asia, more liberal algorithms result in mistaken merges.
Automatic normalization of author names is not exact. Hence it is clear that manual intervention based on human knowledge is required to perfect algorithmic results. ACM is meeting this challenge, continuing to work to improve the automated merges by tweaking the weighting of the evidence in light of experience.
- Bibliometrics: In 1926, Alfred Lotka formulated his power law (known as Lotka's Law) describing the frequency of publication by authors in a given field. According to this bibliometric law of scientific productivity, only a very small percentage (~6%) of authors in a field will produce more than 10 articles while the majority (perhaps 60%) will have but a single article published. With ACM's first cut at author name normalization in place, the distribution of our authors with 1, 2, 3..n publications does not match Lotka's Law precisely, but neither is the distribution curve far off. For a definition of ACM's first set of publication statistics, see Bibliometrics
- Future Direction:
The initial release of the Author Edit Screen is open to anyone in the community with an ACM account, but it is limited to personal information. An author's photograph, a Home Page URL, and an email may be added, deleted or edited. Changes are reviewed before they are made available on the live site.
ACM will expand this edit facility to accommodate more types of data and facilitate ease of community participation with appropriate safeguards. In particular, authors or members of the community will be able to indicate works in their profile that do not belong there and merge others that do belong but are currently missing.
A direct search interface for Author Profiles will be built.
An institutional view of works emerging from their faculty and researchers will be provided along with a relevant set of metrics.
It is possible, too, that the Author Profile page may evolve to allow interested authors to upload unpublished professional materials to an area available for search and free educational use, but distinct from the ACM Digital Library proper. It is hard to predict what shape such an area for user-generated content may take, but it carries interesting potential for input from the community.
Bibliometrics
The ACM DL is a comprehensive repository of publications from the entire field of computing.
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. 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