Collective semantic behavior extraction in social networks

Abstract As a media for sharing knowledge, forming communities with similar hobbies and interacting with friends, social networks are booming dramatically recently. The study on collective behaviors in social networks is a key to analyze community dynamics and network functionalities. Hence, it is significant and necessary to accurately extract and analyze these collective behaviors. At present, the semantic behaviors of any individual in semantic social networks can be extracted conveniently. However, how to automatically extract collective semantic behaviors, to a large scale, in social networks is still an open question. Our proposed collective semantic behavior extraction process works as follows: Firstly, as for the popular semantic social networks, such as Facebook, Twitter and QQ, it is convenient to extract semantic behaviors from any semantic information. Secondly, as similar behaviors will form a community spontaneously, the communities with similar extracted semantic behaviors can be determined with DeepWalk. Hence, as for a determined community, our proposed collective semantic behavior extraction approach can properly extract the collective semantic behaviors in social networks. The experimental results of our proposed approach executed on real semantic information show that our proposed approach can automatically extract collective semantic behaviors accurately.

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