This study of collective behavior is to understand how individuals behave in a social networking environment. Oceans of data generated by social media like Face book, Twitter, Flicker, and YouTube present opportunities and challenges to study collective behavior on a large scale. In this work, we aim to learn to predict collective behavior in social media. In particular, given information about some individuals, how can we infer the behavior of unobserved individuals in the same network? A social-dimension-based approach has been shown effective in addressing the heterogeneity of connections presented in social media.
However, the networks in social media are normally of colossal size, involving hundreds of thousands of actors. The scale of these networks entails scalable learning of models for collective behavior prediction. To address the scalability issue, we propose an edge-centric clustering scheme to extract sparse social dimensions. With sparse social dimensions, the proposed approach can efficiently handle networks of millions of actors while demonstrating a comparable prediction performance to other non-scalable methods.
As existing approaches to extract social dimensions suffer from scalability, it is imperative to address the scalability issue. Connections in social media are not homogeneous. People can connect to their family, colleagues, college classmates, or buddies met online. Some relations are helpful in determining a targeted behavior while others are not. This relation-type information, however, is often not readily available in social media. A direct application of collective inference or label propagation would treat connections in a social network as if they were homogeneous.
A recent framework based on social dimensions is shown to be effective in addressing this heterogeneity. The framework suggests a novel way of network classification: first, capture the latent affiliations of actors by extracting social dimensions based on network connectivity, and next, apply extant data mining techniques to classification based on the extracted dimensions.
In the initial study, modularity maximization was employed to extract social dimensions. The superiority of this framework over other representative relational learning methods has been verified with social media data in. The original framework, however, is not scalable to handle networks of colossal sizes because the extracted social dimensions are rather dense. In social media, a network of millions of actors is very common. With a huge number of actors, extracted dense social dimensions cannot even be held in memory, causing a serious computational problem.
Sparsifying social dimensions can be effective in eliminating the scalability bottleneck. In this work, we propose an effective edge-centric approach to extract sparse social dimensions. We prove that with our proposed approach, sparsity of social dimensions is guaranteed.
- Social Dimension Extraction
- Discriminative Learning
- Chart Generation For Group/Month
- Chart Generation For User/Group
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