NDLI: Expanding network communities from representative examples

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ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 11

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 10

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 9

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 8

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 7

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 6

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 5

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 4

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 3

Issue 4, November 2009

Issue 3, July 2009

Issue 2, April 2009

Introduction to special issue on social computing, behavioral modeling, and prediction

Expanding network communities from representative examples

Analyzing communities and their evolutions in dynamic social networks

Blocking links to minimize contamination spread in a social network

Modeling information-seeker satisfaction in community question answering

Issue 1, March 2009

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 2

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 1

Expanding network communities from representative examples

Content Provider	ACM Digital Library
Author	Skiena, Steven Mehler, Andrew
Copyright Year	2009
Abstract	We present an approach to leverage a small subset of a coherent community within a social network into a much larger, more representative sample. Our problem becomes identifying a small conductance subgraph containing many (but not necessarily all) members of the given seed set. Starting with an initial seed set representing a sample of a community, we seek to discover as much of the full community as possible. We present a general method for network community expansion, demonstrating that our methods work well in expanding communities in real world networks starting from small given seed groups (20 to 400 members). Our approach is marked by incremental expansion from the seeds with retrospective analysis to determine the ultimate boundaries of our community. We demonstrate how to increase the robustness of the general approach through bootstrapping multiple random partitions of the input set into seed and evaluation groups. We go beyond statistical comparisons against gold standards to careful subjective evaluations of our expanded communities. This process explains the causes of most disagreement between our expanded communities and our gold-standards—arguing that our expansion methods provide more reliable communities than can be extracted from reference sources/gazetteers such as Wikipedia.
Starting Page	1
Ending Page	27
Page Count	27
File Format	PDF
ISSN	15564681
e-ISSN	1556472X
DOI	10.1145/1514888.1514890
Volume Number	3
Issue Number	2
Journal	ACM Transactions on Knowledge Discovery from Data (TKDD)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-04-21
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discrete mathematics Artificial intelligence Community discovery Graph theory News analysis Social networks
Content Type	Text
Resource Type	Article
Subject	Computer Science

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

Social Community Analysis via a Factor Graph Model

Community detection and visualization in social networks: Integrating structural and semantic information

Structure and Overlaps of Ground-Truth Communities in Networks

Community Discovery from Social Media by Low-Rank Matrix Recovery

A game-theoretic framework to identify overlapping communities in social networks

A Soft Modularity Function For Detecting Fuzzy Communities in Social Networks

CIM: Community-Based Influence Maximization in Social Networks

A Heuristic Clustering Algorithm for Mining Communities in Signed Networks

Learning recursive languages from good examples

Expanding network communities from representative examples

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Social Community Analysis via a Factor Graph Model

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Structure and Overlaps of Ground-Truth Communities in Networks

Community Discovery from Social Media by Low-Rank Matrix Recovery

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