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Eighth Workshop on Mining and Learning with Graphs

Research output: Journal contributions › Conference abstract in journal › Research › peer-review

Standard

Eighth Workshop on Mining and Learning with Graphs. / Brefeld, Ulf; Getoor, Lise; Macskassy, Sofus A.
In: ACM SIGKDD Explorations, Vol. 12, No. 2, 2010, p. 63-65.

Research output: Journal contributions › Conference abstract in journal › Research › peer-review

Harvard

Brefeld, U, Getoor, L & Macskassy, SA 2010, 'Eighth Workshop on Mining and Learning with Graphs', ACM SIGKDD Explorations, vol. 12, no. 2, pp. 63-65. https://doi.org/10.1145/1964897.1964915

APA

Brefeld, U., Getoor, L., & Macskassy, S. A. (2010). Eighth Workshop on Mining and Learning with Graphs. ACM SIGKDD Explorations, 12(2), 63-65. https://doi.org/10.1145/1964897.1964915

Vancouver

Brefeld U, Getoor L, Macskassy SA. Eighth Workshop on Mining and Learning with Graphs. ACM SIGKDD Explorations. 2010;12(2):63-65. doi: 10.1145/1964897.1964915

Bibtex

@article{b975a21077084364bd47a3af86349fb9,
title = "Eighth Workshop on Mining and Learning with Graphs",
abstract = "The Eighth Workshop on Mining and Learning with Graphs (MLG)1was held at KDD 2010 in Washington DC. It brought together a variete of researchers interested in analyzing data that is best represented as a graph. Examples include the WWW, social networks, biological networks, communication networks, and many others. The importance of being able to effectively mine and learn from such data is growing, as more and more structured and semi-structured data is becoming available. This is a problem across widely different fields such as economics, statistics, social science, physics and computer science, and is studied within a variety of sub-disciplines of machine learning and data mining including graph mining, graphical models, kernel theory, statistical relational learning, etc. The objective of this workshop was to bring together practitioners from these various fields and areas to foster a rich discussion of which problems we work on, how we frame them in the context of graphs, which tools and algorithms we apply and our general findings and lessons learned. This year's workshop was very successful with well over 100 attendees, excellent keynote speakers and papers. This is a rapidly growing area and we believe that this community is only in its infancy.",
keywords = "Informatics, Business informatics",
author = "Ulf Brefeld and Lise Getoor and Macskassy, {Sofus A.}",
year = "2010",
doi = "10.1145/1964897.1964915",
language = "English",
volume = "12",
pages = "63--65",
journal = "ACM SIGKDD Explorations",
issn = "1931-0145",
publisher = "Association for Computing Machinery, Inc",
number = "2",
note = "16th SIGKDD Conference on Knowledge Discovery and Data Mining - 2010, SIGKDD ; Conference date: 24-07-2010 Through 28-07-2010",
url = "http://www.kdd2010.com/program.shtml",

}

RIS

TY - JOUR

T1 - Eighth Workshop on Mining and Learning with Graphs

AU - Brefeld, Ulf

AU - Getoor, Lise

AU - Macskassy, Sofus A.

N1 - Conference code: 16

PY - 2010

Y1 - 2010

N2 - The Eighth Workshop on Mining and Learning with Graphs (MLG)1was held at KDD 2010 in Washington DC. It brought together a variete of researchers interested in analyzing data that is best represented as a graph. Examples include the WWW, social networks, biological networks, communication networks, and many others. The importance of being able to effectively mine and learn from such data is growing, as more and more structured and semi-structured data is becoming available. This is a problem across widely different fields such as economics, statistics, social science, physics and computer science, and is studied within a variety of sub-disciplines of machine learning and data mining including graph mining, graphical models, kernel theory, statistical relational learning, etc. The objective of this workshop was to bring together practitioners from these various fields and areas to foster a rich discussion of which problems we work on, how we frame them in the context of graphs, which tools and algorithms we apply and our general findings and lessons learned. This year's workshop was very successful with well over 100 attendees, excellent keynote speakers and papers. This is a rapidly growing area and we believe that this community is only in its infancy.

AB - The Eighth Workshop on Mining and Learning with Graphs (MLG)1was held at KDD 2010 in Washington DC. It brought together a variete of researchers interested in analyzing data that is best represented as a graph. Examples include the WWW, social networks, biological networks, communication networks, and many others. The importance of being able to effectively mine and learn from such data is growing, as more and more structured and semi-structured data is becoming available. This is a problem across widely different fields such as economics, statistics, social science, physics and computer science, and is studied within a variety of sub-disciplines of machine learning and data mining including graph mining, graphical models, kernel theory, statistical relational learning, etc. The objective of this workshop was to bring together practitioners from these various fields and areas to foster a rich discussion of which problems we work on, how we frame them in the context of graphs, which tools and algorithms we apply and our general findings and lessons learned. This year's workshop was very successful with well over 100 attendees, excellent keynote speakers and papers. This is a rapidly growing area and we believe that this community is only in its infancy.

KW - Informatics

KW - Business informatics

U2 - 10.1145/1964897.1964915

DO - 10.1145/1964897.1964915

M3 - Conference abstract in journal

VL - 12

SP - 63

EP - 65

JO - ACM SIGKDD Explorations

JF - ACM SIGKDD Explorations

SN - 1931-0145

IS - 2

T2 - 16th SIGKDD Conference on Knowledge Discovery and Data Mining - 2010

Y2 - 24 July 2010 through 28 July 2010

ER -

Other publications by the same author(s)

Interactive sequential generative models for team sports

Fassmeyer, D., Cordes, M. & Brefeld, U., 02.2025, In: Machine Learning. 114, 2, 15 p., 38.

Research output: Journal contributions › Journal articles › Research › peer-review

Joint Item Response Models for Manual and Automatic Scores on Open-Ended Test Items

Bengs, D., Brefeld, U., Kroehne, U. & Zehner, F., 2025, (Accepted/In press) In: Psychometrika.

Research output: Journal contributions › Journal articles › Research › peer-review

Machine Learning and Data Mining for Sports Analytics: 11th International Workshop, MLSA 2024, Vilnius, Lithuania, September 9, 2024, Revised Selected Papers

Brefeld, U. (Editor), Davis, J. (Editor), Van Haaren, J. (Editor) & Zimmermann, A. (Editor), 2025, Cham: Springer Verlag. 119 p. (Communications in Computer and Information Science; vol. 2460)

Research output: Books and anthologies › Conference proceedings › Research

Masked autoencoder for multiagent trajectories

Rudolph, Y. & Brefeld, U., 02.2025, In: Machine Learning. 114, 2, 18 p., 44.

Research output: Journal contributions › Journal articles › Research › peer-review

Self-improvement for Computerized Adaptive Testing

Rudolph, Y., Neubauer, K. & Brefeld, U., 2026, Machine Learning and Knowledge Discovery in Databases - Research Track: European Conference, ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Proceedings. Ribeiro, R. P., Jorge, A. M., Soares, C., Gama, J., Pfahringer, B., Japkowicz, N., Larrañaga, P. & Abreu, P. H. (eds.). Cham: Springer International Publishing, Vol. 2. p. 70-86 17 p. (Lecture Notes in Computer Science; vol. 16014 LNCS).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

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