poster

Complex Patterns in Dynamic Attributed Graphs

Authors:

Rina Singh,

Jeffrey A. Graves,

Douglas A. TalbertAuthors Info & Claims

WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web

Pages 105 - 106

https://doi.org/10.1145/2872518.2889374

Published: 11 April 2016 Publication History

Get Access

Abstract

In recent years, there has been huge growth in the amount of graph data generated from various sources. These types of data are often represented by vertices and edges in a graph with real-valued attributes, topological properties, and temporal information associated with the vertices. Until recently, most pattern mining techniques focus solely on vertex attributes, topological properties, or a combination of these in a static sense; mining attribute and topological changes simultaneously over time has largely been overlooked. In this work-in-progress paper, we propose to extend an existing state-of-the-art technique to mine for patterns in dynamic attributed graphs which appear to trigger changes in attribute values.

References

[1]

E. Desmier, M. Plantevit, C. Robardet, and J.-F. Boulicaut. Cohesive co-evolution patterns in dynamic attributed graphs. In Discovery Science, pages 110--124. Springer, 2012.

Crossref

Google Scholar

[2]

E. Desmier, M. Plantevit, C. Robardet, and J.-F. Boulicaut. Trend mining in dynamic attributed graphs. In Machine Learning and Knowledge Discovery in Databases, pages 654--669. Springer, 2013.

Google Scholar

[3]

M. Kaytoue, Y. Pitarch, M. Plantevit, and C. Robardet. Triggering patterns of topology changes in dynamic graphs. In Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on, pages 158--165. IEEE, 2014.

Digital Library

Google Scholar

[4]

A. Prado, M. Plantevit, C. Robardet, and J.-F. Boulicaut. Mining graph topological patterns: Finding covariations among vertex descriptors. Knowledge and Data Engineering, IEEE Transactions on, 25(9):2090--2104, 2013.

Digital Library

Google Scholar

[5]

J. Wang and J. Han. Bide: Efficient mining of frequent closed sequences. In Data Engineering, 2004. Proceedings. 20th International Conference on, pages 79--90. IEEE, 2004.

Digital Library

Google Scholar

Index Terms

Complex Patterns in Dynamic Attributed Graphs
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Mining Attribute Evolution Rules in Dynamic Attributed Graphs
Big Data Analytics and Knowledge Discovery
Abstract
A dynamic attributed graph is a graph that changes over time and where each vertex is described using multiple continuous attributes. Such graphs are found in numerous domains, e.g., social network analysis. Several studies have been done on ...
Supporting the Discovery of Relevant Topological Patterns in Attributed Graphs
ICDMW '12: Proceedings of the 2012 IEEE 12th International Conference on Data Mining Workshops

We propose TopGraphVisualizer, a tool to support the discovery of relevant topological patterns in attributed graphs. It relies on a new pattern detection method that crucially needs for sophisticated post processing and visualization. A topological ...
Mining credible attribute rules in dynamic attributed graphs
Abstract
Graph mining has numerous real-world applications. The goal is to extract interesting subgraphs or patterns in graph databases. Dynamic attributed graphs are more complex databases in which graphs change over time, and each vertex has multiple ...
Highlights
- We proved that the Lift measure is not anti-monotonic.
- A novel anti-monotonic measure called all-conf is designed.
- The all-conf measure can eliminate cross-support core patterns.
- The new algorithm shows high performance when ...

Comments

Information & Contributors

Information

Published In

WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web

April 2016

1094 pages

ISBN:9781450341448

General Chairs:
Jacqueline Bourdeau
Tele-university (TELUQ), Montreal, QC, Canada
,
Jim A. Hendler
Rensselaer Polytechnic Institute, Troy, NY, USA
,
Roger Nkambou Nkambou
Université du Québec à Montréal, Montreal, QC, Canada
,
Program Chairs:
Ian Horrocks
University of Oxford, UK
,
Ben Y. Zhao
University of California at Santa Barbara, CA, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 11 April 2016

Check for updates

Qualifiers

Poster

Conference

WWW '16

Sponsor:

IW3C2

WWW '16: 25th International World Wide Web Conference

April 11 - 15, 2016

Québec, Montréal, Canada

Acceptance Rates

WWW '16 Companion Paper Acceptance Rate 115 of 727 submissions, 16%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

0
Total Citations
152
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Index Terms

Recommendations

Mining Attribute Evolution Rules in Dynamic Attributed Graphs

Supporting the Discovery of Relevant Topological Patterns in Attributed Graphs

Mining credible attribute rules in dynamic attributed graphs

Comments

Information

Published In

Sponsors

In-Cooperation

Publisher

Publication History

Check for updates

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations