The rise in use of the social web has forced web users to duplicate their identity in fragmented information spaces. Commonly these spaces contain rich identity representations hidden within walled garden data silos. This paper presents work to export social graphs from such data silos as RDF datasets, and provide linkage between these social graphs according to a graph matching paradigm. Our work contributes to the linked data movement by providing a decentralised social graph containing linked data describing fragmented identity components.

Abstract. Networks can be represented as evolutionary graphs in a variety of spatio-temporal applications. Changes in the nodes and edges over time may also result in corresponding changes in structural garph properties such as shortest path distances. In this paper, we study the problem of detecting the top-k most significant shortest-path distance changes between two snapshots of an evolving graph. While the problem is solvable with two applications of the all-pairs shortest path algorithm, such a solution would be extremely slow and impractical for very large graphs. This is because when a graph may contain millions of nodes, even the storage of distances between all node pairs can become inefficient in practice. Therefore, it is desirable to design algorithms which can directly determine the significant changes in shortest path distances, without materializing the distances in individual snapshots. We present algorithms that are up to two orders of magnitude faster than such a solution, while retaining comparable accuracy. 1