arXiv subjects

Senzhang Wang

Publications and source records attributed to Senzhang Wang.

75 records · Page 5Linked to original sources

arXiv2015

Location Prediction of Social Images via Generative Model

The vast amount of geo-tagged social images has attracted great attention in research of predicting location using the plentiful content of images, such as visual content and textual description. Most of the existing researches use the text-based or vision-based method to predict location. There still exists a problem: how to effectively exploit the correlation between different types of content as well as their geographical distributions for location prediction. In this paper, we propose to predict image location by learning the latent relation between geographical location and multiple types of image content. In particularly, we propose a geographical topic model GTMI (geographical topic model of social image) to integrate multiple types of image content as well as the geographical distributions, In GTMI, image topic is modeled on both text vocabulary and visual feature. Each region has its own distribution over topics and hence has its own language model and vision pattern. The location of a new image is estimated based on the joint probability of image content and similarity measure on topic distribution between images. Experiment results demonstrate the performance of location prediction based on GTMI.

cs.IR↗

arXiv2015

CENI: a Hybrid Framework for Efficiently Inferring Information Networks

Nowadays, the message diffusion links among users or websites drive the development of countless innovative applications. However, in reality, it is easier for us to observe the timestamps when different nodes in the network react on a message, while the connections empowering the diffusion of the message remain hidden. This motivates recent extensive studies on the network inference problem: unveiling the edges from the records of messages disseminated through them. Existing solutions are computationally expensive, which motivates us to develop an efficient two-step general framework, Clustering Embedded Network Inference (CENI). CENI integrates clustering strategies to improve the efficiency of network inference. By clustering nodes directly on the timelines of messages, we propose two naive implementations of CENI: Infection-centric CENI and Cascade-centric CENI. Additionally, we point out the critical dimension problem of CENI: instead of one-dimensional timelines, we need to first project the nodes to an Euclidean space of certain dimension before clustering. A CENI adopting clustering method on the projected space can better preserve the structure hidden in the cascades, and generate more accurately inferred links. This insight sheds light on other related work attempting to discover or utilize the latent cluster structure in the disseminated messages. By addressing the critical dimension problem, we propose the third implementation of the CENI framework: Projection-based CENI. Through extensive experiments on two real datasets, we show that the three CENI models only need around 20% $\sim$ 50% of the running time of state-of-the-art methods. Moreover, the inferred edges of Projection-based CENI preserves or even outperforms the effectiveness of state-of-the-art methods.

cs.SI↗

arXiv2014

Future Influence Ranking of Scientific Literature

Researchers or students entering a emerging research area are particularly interested in what newly published papers will be most cited and which young researchers will become influential in the future, so that they can catch the most recent advances and find valuable research directions. However, predicting the future importance of scientific articles and authors is challenging due to the dynamic nature of literature networks and evolving research topics. Different from most previous studies aiming to rank the current importance of literatures and authors, we focus on \emph{ranking the future popularity of new publications and young researchers} by proposing a unified ranking model to combine various available information. Specifically, we first propose to extract two kinds of text features, words and words co-occurrence to characterize innovative papers and authors. Then, instead of using static and un-weighted graphs, we construct time-aware weighted graphs to distinguish the various importance of links established at different time. Finally, by leveraging both the constructed text features and graphs, we propose a mutual reinforcement ranking framework called \emph{MRFRank} to rank the future importance of papers and authors simultaneously. Experimental results on the ArnetMiner dataset show that the proposed approach significantly outperforms the baselines on the metric \emph{recommendation intensity}.

cs.SI↗