How others affect your Twitter #hashtag adoption? Examination of community-based and context-based information diffusion in Twitter

Twitter has become a rich source of people’s opinions about a variety of topics, such as their daily life, and current news. Twitter’s retweeting and mentioning mechanisms enable users to disseminate information broadly. In this study, we investigate the effects of community-based and context-based features on the users’ information adoption and diffusion patterns in Twitter. Community-based features capture how the adoption of a hashtag by users within the target user’s community and users outside that community influences the target user’s selection of the target hashtag. Context-based features measure the influence of other users’ adoption of hashtags that are semantically similar with a hashtag on the target user’s adoption of this hashtag. We find the community-based features enhance the prediction of users’ hashtag adoption and diffusion. However, the further exploration of context-based features is needed

Understanding scientific collaboration from the perspective of collaborators and their network structures

Scientific collaboration is one of the key factors to trigger innovations. Coauthorship networks have been taken as representations of scholars’ collaboration for a long time. This study investigates how the authors’ attributes and the coauthorship network structures simultaneously influence the scientific collaboration among them. Exponential random graph models (ERGMs) are adopted in this research. We find that an author has a propensity to coauthor with the other scholar if they have different levels of productivity. We also find that the effect of network’s transitivity strongly influence authors’ collaboration. We demonstrate that taking the effects from both authors’ attributes and the network structures into consideration helps gain a comprehensive understanding of scientific collaboration

BL-MNE: Emerging Heterogeneous Social Network Embedding through Broad Learning with Aligned Autoencoder

Network embedding aims at projecting the network data into a low-dimensional feature space, where the nodes are represented as a unique feature vector and network structure can be effectively preserved. In recent years, more and more online application service sites can be represented as massive and complex networks, which are extremely challenging for traditional machine learning algorithms to deal with. Effective embedding of the complex network data into low-dimension feature representation can both save data storage space and enable traditional machine learning algorithms applicable to handle the network data. Network embedding performance will degrade greatly if the networks are of a sparse structure, like the emerging networks with few connections. In this paper, we propose to learn the embedding representation for a target emerging network based on the broad learning setting, where the emerging network is aligned with other external mature networks at the same time. To solve the problem, a new embedding framework, namely "Deep alIgned autoencoder based eMbEdding" (DIME), is introduced in this paper. DIME handles the diverse link and attribute in a unified analytic based on broad learning, and introduces the multiple aligned attributed heterogeneous social network concept to model the network structure. A set of meta paths are introduced in the paper, which define various kinds of connections among users via the heterogeneous link and attribute information. The closeness among users in the networks are defined as the meta proximity scores, which will be fed into DIME to learn the embedding vectors of users in the emerging network. Extensive experiments have been done on real-world aligned social networks, which have demonstrated the effectiveness of DIME in learning the emerging network embedding vectors.Comment: 10 pages, 9 figures, 4 tables. Full paper is accepted by ICDM 2017, In: Proceedings of the 2017 IEEE International Conference on Data Mining