@article {14485,
	title = {Eighth workshop on mining and learning with graphs},
	journal = {SIGKDD Explor. Newsl.},
	volume = {12},
	year = {2011},
	month = {2011/03//},
	pages = {63 - 65},
	abstract = {The Eighth Workshop on Mining and Learning with Graphs (MLG)1was held at KDD 2010 in Washington DC. It brought together a variete of researchers interested in analyzing data that is best represented as a graph. Examples include the WWW, social networks, biological networks, communication networks, and many others. The importance of being able to effectively mine and learn from such data is growing, as more and more structured and semi-structured data is becoming available. This is a problem across widely different fields such as economics, statistics, social science, physics and computer science, and is studied within a variety of sub-disciplines of machine learning and data mining including graph mining, graphical models, kernel theory, statistical relational learning, etc. The objective of this workshop was to bring together practitioners from these various fields and areas to foster a rich discussion of which problems we work on, how we frame them in the context of graphs, which tools and algorithms we apply and our general findings and lessons learned. This year{\textquoteright}s workshop was very successful with well over 100 attendees, excellent keynote speakers and papers. This is a rapidly growing area and we believe that this community is only in its infancy. We hope that the readers will join us next year for MLG 2011.},
	keywords = {data mining, dynamic network analysis, graph mining, kernel methods, link mining, machine learning, network analysis, pattern recognition, relational learning, scalable graph mining, statistical relational learning},
	isbn = {1931-0145},
	doi = {10.1145/1964897.1964915},
	url = {http://doi.acm.org/10.1145/1964897.1964915},
	author = {Brefeld,Ulf and Getoor, Lise and Macskassy,Sofus A.}
}
@conference {14469,
	title = {Exploiting statistical and relational information on the web and in social media},
	booktitle = {Proceedings of the fourth ACM international conference on Web search and data mining},
	series = {WSDM {\textquoteright}11},
	year = {2011},
	month = {2011///},
	pages = {9 - 10},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {The popularity of Web 2.0, characterized by a proliferation of social media sites, and Web 3.0, with more richly semantically annotated objects and relationships, brings to light a variety of important prediction, ranking, and extraction tasks. The input to these tasks is often best seen as a (noisy) multi-relational graph, such as the click graph, defined by user interactions with Web sites; and the social graph, defined by friendships and affiliations on social media sites. This tutorial will provide an overview of statistical relational learning and inference techniques, motivating and illustrating them using web and social media applications. We will start by briefly surveying some of the sources of statistical and relational information on the web and in social media and will then dedicate most of the tutorial time to an introduction to representations and techniques for learning and reasoning with multi-relational information, viewing them through the lens of web and social media domains. We will end with a discussion of current trends and related fields, such as privacy in social networks.},
	keywords = {graph analysis, probabilistic inference, query log data, social media, statistical relational learning},
	isbn = {978-1-4503-0493-1},
	doi = {10.1145/1935826.1935834},
	url = {http://doi.acm.org/10.1145/1935826.1935834},
	author = {Getoor, Lise and Mihalkova,Lilyana}
}
@conference {14457,
	title = {Learning statistical models from relational data},
	booktitle = {Proceedings of the 2011 international conference on Management of data},
	series = {SIGMOD {\textquoteright}11},
	year = {2011},
	month = {2011///},
	pages = {1195 - 1198},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Statistical Relational Learning (SRL) is a subarea of machine learning which combines elements from statistical and probabilistic modeling with languages which support structured data representations. In this survey, we will: 1) provide an introduction to SRL, 2) describe some of the distinguishing characteristics of SRL systems, including relational feature construction and collective classification, 3) describe three SRL systems in detail, 4) discuss applications of SRL techniques to important data management problems such as entity resolution, selectivity estimation, and information integration, and 5) discuss connections between SRL methods and existing database research such as probabilistic databases.},
	keywords = {machine learning, probabilistic graphical models, statistical relational learning},
	isbn = {978-1-4503-0661-4},
	doi = {10.1145/1989323.1989451},
	url = {http://doi.acm.org/10.1145/1989323.1989451},
	author = {Getoor, Lise and Mihalkova,Lilyana}
}
@conference {14451,
	title = {Identifying graphs from noisy and incomplete data},
	booktitle = {Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data},
	series = {U {\textquoteright}09},
	year = {2009},
	month = {2009///},
	pages = {23 - 29},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {There is a growing wealth of data describing networks of various types, including social networks, physical networks such as transportation or communication networks, and biological networks. At the same time, there is a growing interest in analyzing these networks, in order to uncover general laws that govern their structure and evolution, and patterns and predictive models to develop better policies and practices. However, a fundamental challenge in dealing with this newly available observational data describing networks is that the data is often of dubious quality -- it is noisy and incomplete -- and before any analysis method can be applied, the data must be cleaned, and missing information inferred. In this paper, we introduce the notion of graph identification, which explicitly models the inference of a "cleaned" output network from a noisy input graph. It is this output network that is appropriate for further analysis. We present an illustrative example and use the example to explore the types of inferences involved in graph identification, as well as the challenges and issues involved in combining those inferences. We then present a simple, general approach to combining the inferences in graph identification and experimentally show the utility of our combined approach and how the performance of graph identification is sensitive to the inter-dependencies among these inferences.},
	keywords = {classification, data mining, entity resolution, link prediction, social networks, statistical relational learning},
	isbn = {978-1-60558-675-5},
	doi = {10.1145/1610555.1610559},
	url = {http://doi.acm.org/10.1145/1610555.1610559},
	author = {Namata,Jr.,Galileo Mark S. and Getoor, Lise}
}
