Multi-Source Network Fusion and Analysis (MSNFA 2018)

Scope

   With advances in technology, in social media big data, users usually participate in different social media platforms. Merging data from different sources is a more accurate and effective way to mine and analyses behavior of user and group. Moreover, users in a single data source are usually interconnected with each other to form a relational network. Multiple data sources depict user behavior from different aspects, thus forming a multi-view, multi-source, multi-model network and otherwise. For example, the same users can appear in many social media platforms. Linkedin profiles user’s career information, Twitter and Sina Weibo provide user’s living information, and StackOverfolw provides user’s professional information. Most real-world applications can be modeled with multi-layer network, hypergraph and so on which include different networks based on different source. The multi-source network analysis is an accuracy and effective way to comprehensively analyses social network. Meanwhile, multi-source information network fusion and analysis face some challenges, such as how to merge different networks into a unified network without loss of information, how to identify the same object from different networks, and how to align different networks.

   The emphasis of this workshop shall be analysis approaches and applications based on multi-source, multi-view and multi-model network. This workshop shall help to bring together people from these different areas and present an opportunity for researchers and practitioners to share new techniques for multi-source network fusion and analysis. Contributions that push the state of the art in all facets of multi-source network are encouraged and welcomed.


WORKSHOP AREAS

  Topics of interest include but not limited to:

  1.  Multi-network fusion from big data
  2.  Multi-source network Representation Learning
  3.  Multi-source network Mining (e.g., clustering, classification and recommendation)
  4.  Multi-source network analysis (e.g., Role detection, Information diffusion, Node influence, Community detection and evolution of network)
  5.  Network Alignment
  6.  Multi-source networks link prediction
  7.  Data mining based on Heterogeneous Information Network (e.g., knowledge graph)
  8.  Parallel computing for Multi-source network
  9.  Multi-source network analysis based applications for profiling, social network analysis and multimedia
  10. Semantic mining on Multi-source network


PAPER SUBMISSION

  All submissions should be in English. All submissions must be prepared in the IEEE camera-ready format and submitted through the system same as ICDSC 2018. Only submissions in PDF format are accepted. Research paper submissions are limited to 10 pages. A paper submitted to MSNFA 2018 cannot be under review for any other conference or journal during the entire period that it is considered for MSNFA 2018, and must be substantially different from any previously published work. Submissions are reviewed in a single-blind manner. Please note that all submissions must strictly adhere to the IEEE templates as provided in http://www.ieee-dsc.org/2018/CallForPapers.html.

  Submit paper: https://cmt3.research.microsoft.com/MSNFA2018


IMPORTANT DATES

  Full paper due: April 10, 2018. extend to April 17, 2018
  Acceptance notification: April 27, 2018. extend to May 4, 2018
  Camera-ready copy: May 12, 2018
  Conference Date: June 17, 2018

ORGANIZATION

GENERAL CO-CHAIRS

Bin WuBeijing University of Posts and Telecommunications, China
Chuan ShiBeijing University of Posts and Telecommunications, China
Xiaoli LiInstitute for Infocomm Research , A*STAR, Singapore

Program Committee

Jiuming Huang, National University of Defense Technology, China
Fuzheng Zhuang, Institute of Computing Technology, Chinese Academy of Sciences, China
Xi Zhang, Beijing University of Posts and Telecommunications, China
Hongxin Hu, Clemson University, USA
Shenghua Liu, Institute of Computing Technology, Chinese Academy of Sciences, China
Zhaohui Peng, Shandong University, China
Ning Yang, Sichuan University, China
Senzhang Wang, Beihang University, China
Xin Li, Beijing Institute of Technology, China



If you have any question on MSNFA 2018, please feel free to contact Nianwenning@bupt.edu.cn.
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