Summarization of social activity over time: people, actions and concepts in dynamic networks

We present a framework for automatically summarizing social group activity over time. The problem is important in understanding large scale online social networks, which have diverse social interactions and exhibit temporal dynamics. In this work we construct summarization by extracting activity themes. We propose a novel unified temporal multi-graph framework for extracting activity themes over time. We use non-negative matrix factorization (NMF) approach to derive two interrelated latent spaces for users and concepts. Activity themes are extracted from the derived latent spaces to construct group activity summary. Experiments on real-world Flickr datasets demonstrate that our technique outperforms baseline algorithms such as LSI, and is additionally able to extract temporally representative activities to construct meaningful group activity summary.