Data fusion is the key step for big data analysis. Particularly, in social media big data, users usually participate in different social media platforms. Merging data from different sources is an effective way to depict user’s behavior comprehensively. Moreover, users in a single data source are usually interconnected with each other to form a relational network. Multiple data sources depict user behavior from different views, thus forming a multi-view network. For example, the same users can appear in many social media platforms. Linkedin profiles user’s career information, Twitter and Sina Weibo provide user’s living information, and StackOverfolw provides user’s professional information. Most real-world applications can be modeled with multi-view network which include different networks based on different views. The multi-view network analysis is an effective way to solve the problem of data fusion. Meanwhile, multi-view network fusion and analysis face some challenges, such as how to merge different networks into a unified network, how to identify the same object from different networks, and how to align different networks.

The emphasis of this workshop shall be analysis approaches and applications based on multi-view network. This workshop shall help to bring together people from these different areas and present an opportunity for researchers and practitioners to share new techniques for multi-view network fusion and analysis. Contributions that push the state of the art in all facets of multi-view network are encouraged and welcomed.