Call for Papers

Text Box: RELEVENT TOPICS INCLUDE BUT ARE NOT LIMITED TO: Behavior modeling and analysis in social networks: to improve our understanding of (large-scale) human behavior in social networks and to model user dynamics to demonstrate how such understanding can help improve information and communication system performance. Collective intelligence and consensus formation in large scale networks which consist of both humans and machines. Cognitive modeling, machine learning/understanding, and synthesizing social phenomenon and events, including, e.g., game theoretic and Bayesian social learning; analysis of social learning phenomenon such as rational herding. Securing mechanism and privacy in social networks: to understand how interactions in social networks may post security/privacy issues and how to deal with them, and how to develop trust/belief model/evaluation/framework. Applications of social networks: to understand how the concepts of social networks can help improve our modeling and analysis of traditional problems where interactions of multiple users and systems can be considered as social networks. Examples include: Peer-to-peer streaming, signal processing, and communications Multi-user information theory Multi-user rate and resource allocation Mobile, sensor, or human networks Database and content retrieval

There are currently two major trends towards social networks where signal and information processing are playing an increasing role:

Mobile sensors: As pointed out in a recent Nature article , the single, most important source of data is the ubiquitous mobile phone. Every time a person uses a mobile phone, a few bits of information can be collected; including geographic information, physical activity; the phone’s signal processing hardware can analyze the user’s speaking patterns.

Internet-based social communities: We are witnessing the emergence of large-scale social network communities such as Napster_©, Facebook_©, Twitter_©, and YouTube_© where millions of users form a dynamically-changing infrastructure to share content. Such proliferation and introduction of the new concept of web-based social networking creates a technological revolution not only for the personal and entertainment purposes, but also for many new applications of government/school/industry/research that bring new experiences to users.

In both cases, the massive content production poses new challenges to the scalable and reliable sharing of (multimedia) content over large and heterogeneous networks. While demanding effective management of enormous amounts of unstructured content that users create, share, link and reuse, this also raises critical issues of intellectual property protection and privacy. In large-scale social networks, millions of users actively interact with each other, and such user dynamics not only influence each individual user but also affect the system performance. To provide a predictable and satisfactory level of service, it is important to analyze the impact of human factors on multimedia social networks, and to provide important guidelines to better design of multimedia systems. Similarly, economists are making progress toward understanding social learning, asking how networked agents can form a consensus in their estimates or actions given state measurements.

The goal of IEEE-THEMES is to encourage researchers from different areas (signal processing, information management, computer sciences, and psycho-sociology) to come together to explore and understand the impact of signal and information processing for the emerging research field of social networks, and ultimately to design systems with more efficient, secure, context-aware, and personalized services.

Napster, is the registered trademark of Napster, LLC. Facebookis the registered trademark of Facebook, Inc. Twitter is the registered trademark of Twitter, Inc. YouTube is a registered trademark of Google, Inc.

Call for Papers

IEEE - Thematic Meetings on Signal Processing

November 15, 2009 — Papers Due