NDLI: ABACUS: frequent pAttern mining-BAsed Community discovery in mUltidimensional networkS

Content Provider	Springer Nature Link
Author	Berlingerio, Michele Pinelli, Fabio Calabrese, Francesco
Copyright Year	2013
Abstract	Community discovery in complex networks is the problem of detecting, for each node of the network, its membership to one of more groups of nodes, the communities, that are densely connected, or highly interactive, or, more in general, similar, according to a similarity function. So far, the problem has been widely studied in monodimensional networks, i.e. networks where only one connection between two entities may exist. However, real networks are often multidimensional, i.e., multiple connections between any two nodes may exist, either reflecting different kinds of relationships, or representing different values of the same type of tie. In this context, the problem of community discovery has to be redefined, taking into account multidimensional structure of the graph. We define a new concept of community that groups together nodes sharing memberships to the same monodimensional communities in the different single dimensions. As we show, such communities are meaningful and able to group nodes even if they might not be connected in any of the monodimensional networks. We devise frequent pAttern mining-BAsed Community discoverer in mUltidimensional networkS (ABACUS), an algorithm that is able to extract multidimensional communities based on the extraction of frequent closed itemsets from monodimensional community memberships. Experiments on two different real multidimensional networks confirm the meaningfulness of the introduced concepts, and open the way for a new class of algorithms for community discovery that do not rely on the dense connections among nodes.
Starting Page	294
Ending Page	320
Page Count	27
File Format	PDF
ISSN	13845810
Journal	Data Mining and Knowledge Discovery
Volume Number	27
Issue Number	3
e-ISSN	1573756X
Language	English
Publisher	Springer US
Publisher Date	2013-07-18
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Community discovery Multidimensional networks Social network analysis Data Mining and Knowledge Discovery Artificial Intelligence (incl. Robotics) Information Storage and Retrieval Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Information Systems Computer Science Applications

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

Frequent subgraph mining in outerplanar graphs

Frequent pattern mining: current status and future directions

Nearly exact mining of frequent trees in large networks

Finding Frequent Patterns in a Large Sparse Graph*

Using trees to mine multirelational databases

Incremental sequence-based frequent query pattern mining from XML queries

Improving constrained pattern mining with first-fail-based heuristics

Efficient and effective community search

Mining top-K frequent itemsets through progressive sampling

ABACUS: frequent pAttern mining-BAsed Community discovery in mUltidimensional networkS

Similar Documents

Frequent subgraph mining in outerplanar graphs

Frequent pattern mining: current status and future directions

Nearly exact mining of frequent trees in large networks

Finding Frequent Patterns in a Large Sparse Graph*

Using trees to mine multirelational databases

Incremental sequence-based frequent query pattern mining from XML queries

Improving constrained pattern mining with first-fail-based heuristics

Efficient and effective community search

Mining top-K frequent itemsets through progressive sampling

ABACUS: frequent pAttern mining-BAsed Community discovery in mUltidimensional networkS