Graph kernels have become an established and widely-used technique for solving
classification tasks on graphs. This survey gives a comprehensive overview of techniques …

Graph kernels have emerged as a powerful tool for graph comparison. Most existing graph
kernels focus on local properties of graphs and ignore global structure. In this paper, we …

Owning to the massive growth in internet connectivity, smartphone technology, and digital
tools, the use of various online social networks (OSNs) has significantly increased. On the …

AMiner 1 is a free online academic search and mining system, having collected more than
130,000,000 researcher profiles and over 200,000,000 papers from multiple publication …

Graph kernels have attracted a lot of attention during the last decade, and have evolved into
a rapidly developing branch of learning on structured data. During the past 20 years, the …

In real-world, our DNA is unique but many people share names. This phenomenon often
causes erroneous aggregation of documents of multiple persons who are namesake of one …

Big data is typically characterized with 4V's: Volume, Velocity, Variety and Veracity. When it
comes to big graphs, these challenges become even more staggering. Each and every of …

Applications of machine learning methods increasingly deal with graph structured data
through kernels. Most existing graph kernels compare graphs in terms of features defined on …

Author name ambiguity causes inadequacy and inconvenience in academic information
retrieval, which raises the necessity of author name disambiguation (AND). Existing AND …

The entity reconciliation (ER) problem aroused much interest as a research topic in today's
Big Data era, full of big and open heterogeneous data sources. This problem poses when …