Keynote Speakers


Speaker: Prof. Hiroshi Motoda, Osaka University

Topic: Opinion Formation by Voter Models in Social Networks

Abstract:

Large scale social networking applications have made it possible for news, ideas, opinions and rumors to spread easily, which affects and changes our daily life style substantially. Massive data are constantly being produced and are made available to us, enabling the study of the spread of influence in social networks. Much of the work has treated information as one entity and nodes in the network are either active (influenced) or inactive (uninfluenced), i.e., there are only two states. In this work, we address a different type of information diffusion, which is ``opinion formation'', i.e., spread of opinions. This requires a model that handles multiple states. Since each opinion (what is said) has its own value and an opinion with a higher value propagates more easily/rapidly, we first extend the basic voter model to be able to handle multiple opinions, and incorporate the value for each opinion. We call this model the value-weighted voter (VwV) model. We learn the weight from a limited number of opinion propagation data and predict the future share. We further added a new component to the VwV model reflecting the fact that there are always people that do not agree with the majority, i.e. anti-majoritarians. The model is called the value-weighted mixture voter (VwMV) model which combines the VwV and the anti-voter models both with multiple opinions. We also learn the weight and the anti-majoritarian tendency from the data. Learning the anti-majoritarian tendency is much more difficult than learning the weight, but we show that both are learnable from the data. We carry out the mean field analysis to VwMV model to gain an insight into the average behavior of opinion share and find some interesting features. Finally, we address the problem of detecting the change in opinion share caused by an unknown external situation change under the VwV model with multiple opinions in a retrospective setting. This is the double loop learning problem and the brute force approach is infeasible. We show that the use of the first order derivative of the log liklihood results in much faster solution.

Short Bio:

Hiroshi Motoda is a professor emeritus of Osaka University. His original research background is nuclear engineering, but for the last 25 years he has been working in the area of artificial intelligence, scientific knowledge discovery, knowledge acquisition, machine learning, data mining and information diffusion. He received his Bs, Ms and PhD degrees all in nuclear engineering from the University of Tokyo. He is a member of the the steering committee of PAKDD, PRICAI, DS and ACML. He received the best paper awards from Atomic Energy Society of Japan (1977, 1984) and from Japanese Society of Artificial Intelligence (1989, 1992, 2001), the outstanding achievement awards from JSAI (2000), the distinguished contribution award for PAKDD (2006), Okawa Publication Prize from Okawa Foundation (2007) and outstanding contribution award from Web Intelligence Consortium (2008). He is a fellow of JSAI.

Speaker: Prof. Yanchun Zhang, Centre for Applied Informatics, Victoria University, Australia

Topic: Social networking meets recommender systems

Abstract:

Recently, with the popularity and development of innovative Web technologies, for example, Web 2.0, more and more advanced Web data based services and applications are emerging for Web users to easily generate and distribute Web contents, and conveniently share information in a collaborative environment. These newly enhanced Web functionalities make it possible for Web users to share and locate the needed Web contents easily, to collaborate and interact with each other socially, and to realize knowledge utilization and management freely on the Web. Two typical social Web service are Facebook and Twitter, which are becoming a global and influential information sharing and exchanging platform and data source in the world. As a result, Social Networks is becoming a newly emerging research topic in Web research although this term has appeared in social science, especially psychology in several decades ago.
On the other hand, despite of the Web-based data management research results in developments of many useful Web applications or services, like search engines, users are still facing the problems of information overload and drowning due to the significant and rapid growth in amount of information and the number of users. In particular, Web users usually suffer from the difficulties of finding desirable and accurate information on the Web due to two problems of low precision and low recall caused by above reasons. Recommender system is a specialized process that predicts user preference and recommends customized contents. Due the predominant requirement of personalized service, recommender systems have attracted a large amount of research attention in past decades.
In this talk, we aim to present a landscape of research advances in these two areas of social networking and recommendation systems, covering topics of link analysis and community detection, web mining, social-enhanced recommender systems, emergent event detection in social media, and outline some interesting research directions such as link prediction, social ranking, SNA in recommendation and personalized search.

Short Bio:

Yanchun Zhang is a Professor and the Director of Centre for Applied Informatics at Victoria University, leading a multidisciplinary e-research program across the University. CAI’s program focuses on application driven and multidisciplinary research involving collaboration among experts from different fields, particularly in the ICT area and its applications in health care, community, business, and environmental studies. He obtained a PhD degree in Computer Science from The University of Queensland in 1991. Since then he has been an academic member at The University of Queensland, The University of Southern Queensland and Victoria University. Prof. Zhang is an international expert in databases, data mining, health informatics, web information systems, and web services. He has published over 220 research papers in international journals and conferences proceedings, and authored/edited 12 books. His research has been supported by a number of Australian Research Council's project grants. His research has made some significant impacts on society. For example, the multidisciplinary research into e-health has produced software systems and mapping tools to assist relevant government/industry organisations establish health needs, allowing the development of policy based on firm evidence. Prof Zhang is the Editor-In-Chief of World Wide Web journal (Springer), and Health Information Science and Systems Journal (BioMed Central). He is Chairman of the International Web Information Systems Engineering Society (WISE Society).
Home page: http://www.vu.edu.au/contact-us/yanchun-zhang