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Mining Data Semantics in Heterogeneous Information Networks Workshop (MDS' 2013) -- in conjunction with SIGKDD 2013, August 11th
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Objectives
Now we are living in the big data era and facing big data challenges. Large and complex data are generated almost everywhere and grow exponentially. It is becoming difficult to handle them by simply utilizing traditional data management and data discovery techniques. The workshop aims to discuss key issues and practices of mining semantics in large scale heterogeneous information networks. Mining semantics from heterogeneous networks can address several important issues, including (1) knowledge graph: how to efficiently and effectively extract entities from unstructured data, how to connect data by identifying hidden relationships between data, and how to from the giant knowledge graph to facilitate better search and provide high quality Q&A service; (2) semantic network behavior: how users' personalized behaviors can be discovered from heterogeneous networks, how users are connected via different types of social relationships, and what are the fundamental structures underlying the networks; (3) search big graph: what are innovative ways to search heterogeneous graph, how to handle the heterogeneity on edges and nodes of such graph, and how to make graph search efficient. The workshop will provide a forum for researchers and practitioners from all over the world to share information on their latest investigations. The broader context of the workshop can be related in some respects to the areas of Web Mining, Social Networks Analysis, Semantic Web, Information Retrieval, and Natural Language Processing.
Topics of interest include but are not limited to:
knowledge graph construction;
Semantic integration on entities;
Entity extraction and entity relationship recognition;
Semantic search over heterogeneous networks;
Question answer system based on knowledge graphs;
Clustering (including bi-clustering) and ranking methods for heterogeneous networks;
Pattern-analysis methods (e.g., discriminative frequent pattern analysis);
Novel network analysis algorithms;
New information network analysis or link mining methods;
Mining with domain knowledge;
Algorithms for semantic graph mining and light-weight reasoning;
Personalized knowledge graph;
Domain specific mining (e.g., Life Science and Health Care);
Multi-scale semantic visualization;
Semantic-based social network analysis and collective intelligence mining;
Classification and prediction in social and information networks;
Evolution and information diffusion in social and information networks;
Multidimensional (OLAP) analysis in social and information networks;
Similarity search and intelligent query answering in social and information networks;
Link / relationship prediction and recommendation.
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Schedule Return
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Invited Speaker Return to Top
Filippo Menczer, Indiana University
Title: Information dynamics in social networks
Abstract: This talk presents ongoing work on the study of dynamic processes of and on social networks. The focus is on the diffusion of memes in techno-social information networks, such as microblogs and online social media. We discuss how the spread of memes affects and is in turn affected by the evolving network structure, our limited attention, and the formation of communities. We show that efficient information gathering is a key mechanism behind the creation of links. As users become more active, popular, and influential, they tend to shortcut high-traffic information pathways. The emergence of communities can be interpreted through the reinforcement of collaborative social network activities and communication. The massively heterogeneous popularity and longevity of memes can surprisingly be explained without assuming that they have different intrinsic values, based on the competition for our limited attention combined with network effects. Furthermore, the community structure of our social circles provides essential cues for predicting which memes will go viral.
Bio: Fil Menczer is a Professor of Informatics and Computer Science and the Director of the Center for Complex Networks and Systems Research at the Indiana University School of Informatics and Computing. He also has courtesy appointments in Cognitive Science and Physics, and am affiliated with the Center for Data and Search Informatics and the Biocomplexity Institute. Finally he is the Lagrange Senior Fellow at the ISI Foundation¡¯s Complex Networks Lab in Torino, Italy.
Qiang Yang, Hong Kong University of Science and Technology
Title: waiting for the title
Abstract: waiting for the abstract
Bio: Qiang Yang is a professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology and an IEEE Fellow. His research interests are data mining and artificial intelligence (including automated planning, machine learning and activity recognition). He was an assistant/associate professor at the University of Waterloo between 1989 and 1995, and a professor and NSERC Industrial Research Chair at Simon Fraser University in Canada from 1995 to 2001. His research teams won the 2004 and 2005 ACM KDDCUP international competitions on data mining. He was elected as a vice chair of ACM SIGART in July 2010. He is the founding Editor in Chief of the ACM Transactions on Intelligent Systems and Technology (ACM TIST), and is on the editorial board of IEEE Intelligent Systems and several other international journals.
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Organizers Return to Top
Organizing Committee
- Ying Ding (School of Informatics and Computing, Indiana University)
- Jiawei Han (Department of Computer Science, University of Illinois at Urbana-Champaign)
- Yizhou Sun (College of Computer and Information Science, Northeastern University)
- Jie Tang (Department of Computer Science and Technology, Tsinghua University, China)
- Philip S. Yu (Department of Computer Science, University of Illinouis Chicago)
Program Committee
- Bin Chen (Stanford university)
- Hemant Purohit (Ohio Center of Excellence in Knowledge-enabled Computing)
- Huaiyu Wan (Tsinghua University)
- Jie Tang (Tsinghua University)
- Jing Zhang (Tsinghua University)
- Lili Lin (Hohai University)
- Marie-Francine Moens (Katholieke Universiteit Leuven)
- Victor Lee (John Carroll University)
- Yang Yang (Tsinghua University)
- Yeye He (Microsoft Research)
- Ying Ding (Indiana University)
- Yizhou Sun (Northeastern University)
- Zhanpeng Fang (Tsinghua University)
- Zhixian Yan (Samsung)
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