Algorithms for Mining Uncertain Graph Data - Semantic Scholar
Algorithms for Mining Uncertain Graph Data Jianzhong Li Harbin Institute of Technology China
[email protected]
Abstract With the rapid development of advanced data acquisition techniques such as high-throughput biological experiments and wireless sensor networks, large amount of graph-structured data, graph data for short, have been collected in a wide range of applications. Discovering knowledge from graph data has witnessed a number of applications and received a lot of research attentions. Recently, it is observed that uncertainties are inherent in the structures of some graph data. For example, protein-protein interaction (PPI) data can be represented as a graph, where vertices represent proteins, and edges represent PPI’s. Due to the limits of PPI detection methods, it is uncertain that a detected PPI exist in practice. Other examples of uncertain graph data include topologies of wireless sensor networks, social networks and so on. Managing and mining such large-scale uncertain graph data is of both theoretical and practical significance. Many solid works have been conducted on uncertain graph mining from the aspects of models, semantics, methodology and algorithms in last few years. A number of research papers on managing and mining uncertain graph data have been published in the database and data mining conferences such as VLDB, ICDE, KDD, CIKM and EDBT. This talk focuses on the data model, semantics, computational complexity and algorithms of uncertain graph mining. In the talk, some typical research work in the field of uncertain graph mining will also be introduced, including frequent subgraph pattern mining, dense subgraph detection, reliable subgraph discovery, and clustering on uncertain graph data.
Categories & Subject Descriptors: H. Information Systems: H.2 DATABASE MANAGEMENT: H.2.8 Database applications: Subjects: Data mining Author Keywords:
Graph Mining; Uncertain Data
Bio Jianzhong Li is a professor and the chairman of the Department of Computer Science and Engineering at the Harbin Institute of Technology, China. He worked in the University of California at Berkeley as a visiting scholar in 1985. From 1986 to 1987 and from 1992 to 1993, he was a scientist in the Information Research Group in the Department of Computer Science at Lawrence Berkeley National Laboratory, USA. He was also a visiting professor at the University of Minnesota at Minneapolis, Minnesota, USA, from 1991 to 1992 and from 1998 to 1999. His current research interests include database systems, data intensive super-computing, and wireless sensor networks. He has published more than 200 papers in refereed journals and conference proceedings. He has been involved in the program committees of major computer science and technology conferences, including SIGMOD, VLDB, ICDE, INFOCOM, ICDCS, and WWW. He has also served on the editorial boards for distinguished journals, including Knowledge and Data Engineering, and refereed papers for varied journals and proceedings.
Copyright is held by the author/owner(s). KDD’12, August 12-16, 2012, Beijing, China. ACM 978-1-4503-1462-6 /12/08.
813
[email protected]
Abstract With the rapid development of advanced data acquisition techniques such as high-throughput biological experiments and wireless sensor networks, large amount of graph-structured data, graph data for short, have been collected in a wide range of applications. Discovering knowledge from graph data has witnessed a number of applications and received a lot of research attentions. Recently, it is observed that uncertainties are inherent in the structures of some graph data. For example, protein-protein interaction (PPI) data can be represented as a graph, where vertices represent proteins, and edges represent PPI’s. Due to the limits of PPI detection methods, it is uncertain that a detected PPI exist in practice. Other examples of uncertain graph data include topologies of wireless sensor networks, social networks and so on. Managing and mining such large-scale uncertain graph data is of both theoretical and practical significance. Many solid works have been conducted on uncertain graph mining from the aspects of models, semantics, methodology and algorithms in last few years. A number of research papers on managing and mining uncertain graph data have been published in the database and data mining conferences such as VLDB, ICDE, KDD, CIKM and EDBT. This talk focuses on the data model, semantics, computational complexity and algorithms of uncertain graph mining. In the talk, some typical research work in the field of uncertain graph mining will also be introduced, including frequent subgraph pattern mining, dense subgraph detection, reliable subgraph discovery, and clustering on uncertain graph data.
Categories & Subject Descriptors: H. Information Systems: H.2 DATABASE MANAGEMENT: H.2.8 Database applications: Subjects: Data mining Author Keywords:
Graph Mining; Uncertain Data
Bio Jianzhong Li is a professor and the chairman of the Department of Computer Science and Engineering at the Harbin Institute of Technology, China. He worked in the University of California at Berkeley as a visiting scholar in 1985. From 1986 to 1987 and from 1992 to 1993, he was a scientist in the Information Research Group in the Department of Computer Science at Lawrence Berkeley National Laboratory, USA. He was also a visiting professor at the University of Minnesota at Minneapolis, Minnesota, USA, from 1991 to 1992 and from 1998 to 1999. His current research interests include database systems, data intensive super-computing, and wireless sensor networks. He has published more than 200 papers in refereed journals and conference proceedings. He has been involved in the program committees of major computer science and technology conferences, including SIGMOD, VLDB, ICDE, INFOCOM, ICDCS, and WWW. He has also served on the editorial boards for distinguished journals, including Knowledge and Data Engineering, and refereed papers for varied journals and proceedings.
Copyright is held by the author/owner(s). KDD’12, August 12-16, 2012, Beijing, China. ACM 978-1-4503-1462-6 /12/08.
813
