Integrating Social Media Data of Different Types for Community Detection

1. Integrating Social Media Data of Different Types for Community Detection Jiliang Tang Computer Science and Engineering, Arizona State University, USA {Jiliang.Tang@asu.edu} • Motivation • Link information in social media can be noisy and sparse • Additional sources can provide complemental information , e.g., bookmarking and tagging data indicate user interests, frequency of comments suggests the strength of ties • Since multiple sources can provide a more comprehensive information for users, integrating them should improve the performance of community detection • Experimental Results • Integrating multi-source can significantly improve the performance of community detection • Different sources make uneven contributions to the improvement • The proposed integration framework outperforms other representative methods • Integrating more data sources does not necessary bring better performances • Our Framework • Integrating multi-source can be formulated as the following joint optimization problem: • m is the number of sources, n is the number of nodes and is used to control the sparseness of each column of H • Future work • Since different sources contribute unevenly to the performance, one way to distinct the importance of each source is to weight each source. • Exploring new additional sources of social media data is another promising direction such as incomplete user profiles, short and unconventional text s like tweets • The joint optimization problem is equivalent to the following optimization problem: • and • Contributions • Identifying the need for integrating multiple sources • Proposing a novel framework for multi-source integration • Relating our framework with NMF to show the generality of the framework • Our framework can be interpreted to stack multi-source • Our framework can be applicable to any problems that are suitable for NMF This work was funded, in part, by ONR Data Mining and Machine Learning Lab