BRIDGE 866-453-5550 using participant code 933-8450#SLIDES http://www.800rollcall.com/webpresenter/
Real-life networks are complex, not only having topological structures, but also containing heterogeneous contents and attributes. In order to analyze such networks, users have to master sophisticated computing and programming skills, which indeed becomes a pain point for many scientists and engineers. Thus, there is a need to develop a general graph information system to change the state of the art of graph management and analytics. Specifically, the mixture of structures and contents raises two challenges. First, new types of graph search and mining operations, such as graph aggregation, graph association, and graph pattern mining, are emerging. Second, when graphs become complex and large, most of the existing graph mining algorithms cannot scale well. In this talk, I will give an overview of our efforts that aim to address these challenges. Then I will demonstrate a new kind of association pattern in graphs: proximity pattern, which is significantly different from classic frequent subgraphs. Defined as a set of labels that co-occur in neighborhoods, proximity pattern blurs the boundary between itemset and structure. It is interesting that the information propagation model adopted in proximate pattern mining can be further extended to solving a generic graph alignment problem in multiple networks. In the second half of the talk, I will discuss our recent work on distributed graph processing that could facilitate searching and mining of large-scale networks. The results from these studies will be integrated to formulate the first graph information system.
PRESENTATION: http://www.800rollcall.com/webpresenter/ using participant code 9338450
In online environments social networks play an important role in the transmission of content. Using explicit friendship information gathered from online "buddy" lists, we find that information transmission occurs more rapidly when it follows social ties. However, the items that diffuse most readily through the social network tend to represent niches. Similarly, when it is not just information, but goods and services that are provided across a social network, the seller utilizing these connections enjoys more repeat business, but not more business overall. Although these interesting insights can be gleaned by taking the online social network at face value, there are norms and incentives that affect the accuracy of such networks. In the last part of the talk I will discuss implications of publicness on statements of friendship and trust.
Lada Adamic is an associate professor in the School of Information at the University of Michigan. She is also affiliated with the Center for the Study of Complex Systems and EECS. Her research interests center on information dynamics in networks: how information diffuses, how it can be found, and how it influences the evolution of a network's structure. She worked previously in Hewlett-Packard's Information Dynamics Lab. Her projects have included identifying expertise in online question and answer forums, studying the dynamics of viral marketing, and characterizing the structure in blogs and other online communities.
WebConf https://www.lotuslive.com/join?schedid=8637822 (meeting ID: 8637822)
Information Foraging Theory is a theory of human-information interaction that aims to explain and predict how people will best shape themselves to their information environments, and how information environments can best be shaped to people. The approach involves a kind of reverse engineering in which the theorist asks (a) what is the nature of the task and information environments, (b) why is a given system a good solution to the problem, and (c) how is that "ideal" solution realized (approximated) by mechanism. Typically, the key steps in developing a model of information foraging involve: (a) a rational analysis of the task and information environment (often drawing on optimal foraging theory from biology) and (b) a computational production system model of the cognitive structure of task. I will briefly review work on individual information seeking, and then focus on how this work is being expanded to studies of information production and sensemaking in technology-mediated social systems such as wikis, social tagging, social network sites, and twitter. I will also discuss recent work on integrating information network and social network analysis to identify credible sources of information in twitter.
Peter Pirolli is a Research Fellow in the Augmented Social Cognition Area at the Palo Alto Research Center (PARC), where he has been pursuing studies of human information interaction since 1991. Prior to joining PARC, he was an Associate Professor in the School of Education at UC Berkeley. Pirolli received his doctorate in cognitive psychology from Carnegie Mellon University in 1985. He is an elected Fellow of the American Association for the Advancement of Science, the Association for Psychological Science, the National Academy of Education, and the ACM Computer-Human Interaction Academy.
2. Noshir Contractor (Northwestern University): Multi-theoretical multilevel models to understand and enable networks
Recent advances provide comprehensive digital traces of social actions, interactions, and transactions. These data provide an unprecedented exploratorium to model the socio-technical motivations for creating, maintaining, dissolving, and reconstituting multidimensional social networks. Multidimensional networks include multiple types of nodes (people, documents, datasets, tags, etc.) and multiple types of relationships (co-authorship, citation, web links, etc). Using examples from research in a wide range of activities such as disaster response, public health and massively multiplayer online games (WoW - the World of Warcraft), Contractor will outline a multi-theoretical multilevel model to help advance our ability to understand and enable multidimensional networks.
Noshir Contractor is Jane S. & William J. White Professor of Behavioral Sciences Professor of Ind. Engg & Mgmt Sciences, McCormick School of Engineering Professor of Communication Studies, School of Communication & Professor of Management & Organizations, Kellogg School of Management, Director, Science of Networks in Communities (SONIC) Research Laboratory.
slides (click to download)
video (click to play)
How do graphs look like? How do they evolve over time? How to handle a graph with a billion nodes? We present a comprehensive list of static and temporal 'laws', and some recent observations on real graphs (like, e.g., "eigenSpokes"). For generators, we describe some recent ones, which naturally match all of the known properties of real graphs. Finally, for tools, we present "oddball" for discovering anomalies and patterns, as well as an overview of the PEGASUS system which is designed for handling Billion-node graphs, running on top of the "hadoop" system.
Christos Faloutsos is a Professor at Carnegie Mellon University. He has received the Presidential Young Investigator Award by the National Science Foundation (1989), the Research Contributions Award in ICDM 2006, thirteen ``best paper'' awards, and several teaching awards. He has served as a member of the executive committee of SIGKDD; he has published over 200 refereed articles, 11 book chapters and one monograph. He holds five patents and he has given over 30 tutorials and over 10 invited distinguished lectures. His research interests include data mining for graphs and streams, fractals, database performance, and indexing for multimedia and bio-informatics data.