NDLI: Graph-Based Data Mining in Dynamic Networks: Empirical Comparison of Compression-Based and Frequency-Based Subgraph Mining

Content Provider	IEEE Xplore Digital Library
Author	Chang Hun You Holder, L.B. Cook, D.J.
Copyright Year	2008
Description	Author affiliation: Sch. of Electr. Eng. & Comput. Sci., Washington State Univ., Pullman, WA (Chang Hun You; Holder, L.B.; Cook, D.J.)
Abstract	We propose a dynamic graph-based relational mining approach using graph-rewriting rules to learns patterns in networks that structurally change over time. A dynamic graph containing a sequence of graphs over time represents dynamic properties as well as structural properties of the network. Our approach discovers graph-rewriting rules, which describe the structural transformations between two sequential graphs over time, and also learns description rules that generalize over the discovered graph-rewriting rules. The discovered graph-rewriting rules show how networks change over time, and the description rules in the graph-rewriting rules show temporal patterns in the structural changes. We apply our approach to biological networks to understand how the biosystems change over time. Our compression-based discovery of the description rules is compared with the frequent subgraph mining approach using several evaluation metrics.
Starting Page	929
Ending Page	938
File Size	736185
Page Count	10
File Format	PDF
ISBN	9780769535036
DOI	10.1109/ICDMW.2008.68
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-15
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Graph Mining Conferences Data mining Biological information theory Sun Graph Rewriting Rule Biological cells Computer science USA Councils Biological Network Cells (biology) Frequency Mathematical model Dynamic Graph Analysis
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Graph-based data mining in dynamic networks: Empirical comparison of compression-based and frequency-based subgraph mining (2008)

Learning patterns in the dynamics of biological networks (2009)

Learning patterns in the dynamics of biological networks

Cohesive subgroup model for graph-based text mining

Learning in networks: Complex-valued neurons, pruning, and rule extraction

Multilayer Scene Similarity Assessment

Graph-Based Bioinformatics Mining Research and Application

Mining Large Networks with Subgraph Counting

Mining Frequent Subgraph Patterns from Uncertain Graph Data

Graph-Based Data Mining in Dynamic Networks: Empirical Comparison of Compression-Based and Frequency-Based Subgraph Mining

Similar Documents

Graph-based data mining in dynamic networks: Empirical comparison of compression-based and frequency-based subgraph mining (2008)

Learning patterns in the dynamics of biological networks (2009)

Learning patterns in the dynamics of biological networks

Cohesive subgroup model for graph-based text mining

Learning in networks: Complex-valued neurons, pruning, and rule extraction

Multilayer Scene Similarity Assessment

Graph-Based Bioinformatics Mining Research and Application

Mining Large Networks with Subgraph Counting

Mining Frequent Subgraph Patterns from Uncertain Graph Data

Graph-Based Data Mining in Dynamic Networks: Empirical Comparison of Compression-Based and Frequency-Based Subgraph Mining