We propose a novel graph metric for semantic entity-relationship networks for solving two tasks. First, given a semantic entity-relationship graph such as for example DBpedia we find relevant neighbors for a given query node. Second, we search for paths between two given nodes in order to discover interesting links between the nodes. Compared to using the default step metric our approach yields...

It is essential to predict crowd flow precisely in a city, which practically partitioned into irregular regions based on road networks and functionality. However, prior works mainly focus grid-based prediction, where city divided many regular grids. Although Convolutional Neural Netwok (CNN) powerful capture spatial dependence from Euclidean data, it fails tackle non-Euclidean reflect the corre...

Image-Text Matching is one major task in cross-modal information processing. The main challenge to learn the unified visual and textual representations. Previous methods that perform well on this primarily focus not only alignment between region features images corresponding words sentences, but also relations of regions relational words. However, lack joint learning regional global will cause ...