Scholars' homepages are important places to show personal research interest and academic achievement through the Web. However, according to our observation, only a small portion of scholars update their publications and related events on their homepages in time. In this paper, we propose a homepage augmentation technique, which automatically shows the newest academic events related to a scholar on his/her homepage. Specifically, we model the relations between homepages and the events collected from the Web as a complex heterogenous network, and propose an Embedding-based Heterogenous random Walk algorithm, namely EHWalk, to predict the links between homepages and events. Compared with existing embedding-based link prediction algorithms, EHWalk supports more efficient modeling of complex heterogenous relations in a dynamically changing network, which helps link the massive new updated events to homepages precisely and efficiently. Comprehensive experiments on a real-world dataset are conducted and the results show that our algorithm can achieve both good effectiveness and efficiency for real-world deployment.

Inspired by how search behavior works in human society, we propose CTO, a self-organized semantic overlay based on concept tree for P2P IR infrastructure, which is efficient for full text search in pure P2P environment without any central control or powerful peer as hub node. Especially, CTO performs very well on searching the unpopular resources shared by a few peers. In our experiment, while searching for the scarce documents shared by the peers, CTO achieves about 80% recall rate when the search covers less than 5% peers in the overlay. The search latency of CTO is also very low, which is controlled in the range about 5~12 hops.