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Showing posts with label overlapping community detection. Show all posts
Showing posts with label overlapping community detection. Show all posts

Thursday, 21 November 2013

The 2014 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM '14)

Posted on 01:21 by Unknown

ASONAM '14
The 2014 IEEE/ACM International Conference
on Advances in Social Network Analysis and Mining

Beijing China August 17-20, 2014
Home Page: www.asonam2014.org

Call for Papers

The IEEE/ACM International Conference on Advances in Social Network
Analysis and Mining (ASONAM) provides a premier interdisciplinary
forum to bring together researchers and practitioners from all social
networking analysis and mining related fields for presentation of
original research results, as well as exchange and dissemination of
innovative, practical development experiences. ASONAM 2014 seeks to
address important challenging problems with a specific focus on the
emerging trends and industry needs associated with social networking
analysis and mining. The conference solicits experimental and
theoretical findings along with their real-world applications.

General areas of interest to ASONAM 2014 include the design, analysis
and implementation of social networking theory, systems and
applications from information science, mathematics, communications,
business administration, sociology, psychology, anthropology, applied
linguistics, biology and medicine. More specialized topics within
ASONAM include, but are not limited to:

* Agent based social simulation and computational models
* Anomaly detection in social network evolution
* Application of social network analysis and mining
* Community discovery and analysis in large scale online/offline social networks
* Connection between biological similarities and social network formulation
* Contextual social network analysis and mining
* Crime network analysis
* Crowd sourcing
* Cyber anthropology
* Dark Web
* Data models for social networks and social media
* Data protection inside communities
* Economical impact of social network discovery
* Evolution of communities/patterns on the Web and in large organizations
* Impact of social networks on recommendations systems
* Incorporating social information in query processing and query optimization
* Information acquisition and establishment of social relations
* Influence of cultural aspects on the formation of communities
* Large-scale graph algorithms for social network analysis
* Misbehavior detection in communities
* Migration between communities
* Multi-actor/multiple-relationship networks
* Multi-agent based social network modeling and analysis
* Open source intelligence
* Pattern presentation for end-users and experts
* Personalization for search and for social interaction
* Political impact of social network discovery
* Privacy, security and civil liberty issues
* Recommendations for product purchase, information acquisition and
establishment of social relations
* Scalability of social networking/search algorithms
* Social and cultural anthropology
* Social geography
* Social psychology of information diffusion
* Spatial/knowledge networks
* Statistical modeling of large networks
* Trust networks and evolution of trust
* Visual representation of dynamic social networks

Key Dates
*********

Full paper submission deadline: April 9, 2014
Notification of acceptance: June 9, 2014
Camera-ready papers due: June 24, 2014
Conference events: August 17-20, 2014

Paper Submissions
*****************

Papers reporting original and unpublished research results pertaining
to the above topics are solicited. The papers will reviewed by a
minimum of two subject experts. Full paper manuscripts must be in
English with a maximum length of 8 pages using the IEEE CPS two-column
template. Papers should be submitted to the Conference Web site:
https://wi-lab.com/cyberchair/2014/asonam14/cbc_index.php. IEEE will
retain the copyright for accepted papers and the IEEE copyright form
found at
http://www.ieee.org/publications_standards/publications/rights/copyrightmai
n.html

will therefore have to be completed. Papers will be accepted for the
conference based on the reviewers comments on their originality,
timeliness, significance, relevance, and clarity of presentation. If
the paper is accepted, the paper will appear in the proceedings of the
conference if one author presents the paper at the conference and at
least one author register as a full conference participant.

Publications
************

Accepted and presented papers will be included in the ASONAM 2014
Conference Proceedings and forwarded for inclusion in IEEE Computer
Society Digital Library (CSDL), IEEE Xplore and the ACM Digital
Library. The conference proceedings will be submitted for EI indexing
through INSPEC by the IEEE.

ASONAM 2014 Organizing Committee
********************************

Honorary Chair:
Binxing Fang, Beijing University of Posts and Telecommunications

Conference Chairs:
Yan Jia, National University of Defence Technology, China
Jon Rokne, University of Calgary, Canada

Program Committee Chairs:
Xindong Wu, University of Vermont, USA and Hefei University of Technology, China
Martin Ester, Simon Fraser University, Canada
Guandong Xu, University of Technology Sydney, Australia

Steering Committee Chair:
Reda Alhajj, University of Calgary, Canada

Computing Technology, Chinese Academy of Sciences, China
Daniel Zeng, Institute of Automation, Chinese Academy of Sciences and University of Arizona
Jie Cao, Nanjing University of Finance and Economics, China

Workshops Chairs:
Bettina Berendt, University of Leuven (KU Leuven), Belgium
Jie Tang, Tsinghua University, China
Wolfgan Nejdl, Leibniz Universitat Hannover, Germany

Tutorial Chairs:
V. S. Subrahmanian, University of Maryland, USA
Meng Wang, Hefei University of Technology, China
Carson Leung, Manitoba University, Canada

Panels Chairs:
Alvin Chin, Nokia Research, China
Jing Xu, Peking University, China
Hiroyuki Kitagawa, Tsukuba University, Japan

Demos Chairs:
Tansel Ozyer, TOBB University of Economics and Technology, Turkey
Wei Wang, University of New South Wales, Australia

Publicity Chairs:
Nitin Agarwal, University of Arkansas, USA
Panagiotis Karampelas, Hellenic American University, Greece
Wookey Lee, Inha University, Korea

Publication Chairs:
Xiaohui (Daniel) Tao, University of Southern Queensland, Australia
Min-Yuh Day, Tamkang University, Taiwan

Registration Chair
QIngshun Meng, Beijing University of Posts and Telecommunications, China

Finance Chair
Reda Alhajj, University of Calgary, Canada
Lu Xu, Beijing University of Posts and Telecommunications, China

Web Chair:
Zhiang Wu, Nanjing University of Finance and Economics, China

Further Information:
********************

asonam2014@gmail.com

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Posted in 2014, ACM, ASONAM, call for papers, community detection, conference, ieee, overlapping community detection, social network analysis | No comments

Friday, 18 May 2012

Detecting Overlapping Communities in Facebook with NodeXL

Posted on 00:30 by Unknown
For the initial public offering of Facebook, I re-publish and extend the main outcomes of this RWTH Aachen  master thesis of my student Arber Ceni  in computer science under my supervision.  Supervision was jointly done with  Marc Smith from Connected Action and  Bernie Hogan from Oxford Internet Institute.
MONC
SPLA

Overlapping vs. Non-overlapping Community Detection

Arber has implemented an importer of Facebook data in NodeXL together with two state-of-the-art algorithms for overlapping community detection, i.e. MONC and SPLA.
The visualization is nicely done. Each node is a pie chart where colors code the percentage of community belonging. You can download and install the importer for free.

Overlapping communities are present in social network sites like Facebook. A Facebook user is on average member of seven communities (paper, slides). Computer scientists and social scientists are interested in analyzing communities. They can detect e.g. formation of trends and voter behavior in the data sets or make use of them for personalized advertisement and stock data. Also for system design data can be used for e.g. system optimization.

Overlapping community detection algorithms (OCDA) are a recent further development of community detection algorithms (CDA). CDA always detect disjoint communities in network data, i.e. each node in the network belongs to exactly one community. A community here is defined sloppily as a more dense sub-network with more intracommunity than intercommunity connections. Because of the fact that in real networks disjoint community membership is extremely rare, OCDA have been proposed.

Some good candidates for OCDA are the following:

K-clique percolation method
+ The first algorithm to detect overlapping communities
+ Yields good results
- Different results for different k
- Doesn't have a good time complexity and fails to analyze large networks

GCE
+ It is fast and accurate
+ Yields the best results on LFR benchmark graphs
+ Able to detect non-overlapping communities
- Needs to specify parameters
- Not good results when nodes don't belong to the same number of communities

MONC
+ Time complexity of only O(n2)
+ Uncovers the whole hierarchical structure of the network in a single run
+ Saves time due to merging of communities
+ The core of the algorithm is free of parameters
- Requires three parameters on the post-processing phase
- The complexity is not proven to be O(n2)

SPLA
+ Has a time complexity of only O(Tn), where T = 100
+ Best results on LFR benchmarks
+ Performs well and is very stable over different ranking criteria
+ Can be easily modified to accommodate different speaker/listener rules
+ Can be used to detect fuzzy and crisp overlapping communities.
- Needs a parameter r

Read More
Posted in community detection, Facebook, master thesis, overlapping community detection, RWTH Aachen University, social networks, stcsn | No comments
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