Exploring Vertex Interactions in Ferroelectric Flux-Closure Domains

Using piezoresponse force microscopy, we have observed the progressive development of ferroelectric flux-closure domain structures and Landau−Kittel-type domain patterns, in 300 nm thick single-crystal BaTiO3 platelets. As the microstructural development proceeds, the rate of change of the domain configuration is seen to decrease exponentially. Nevertheless, domain wall velocities throughout are commensurate with creep processes in oxide ferroelectrics. Progressive screening of macroscopic destabilizing fields, primarily the surface-related depolarizing field, successfully describes the main features of the observed kinetics. Changes in the separation of domain-wall vertex junctions prompt a consideration that vertex−vertex interactions could be influencing the measured kinetics. However, the expected dynamic signatures associated with direct vertex−vertex interactions are not resolved. If present, our measurements confine the length scale for interaction between vertices to the order of a few hundred nanometers.

Finding the Most Descriptive Substructures in Graphs with Discrete and Numeric Labels

Many graph datasets are labelled with discrete and numeric attributes. Most frequent substructure discovery algorithms ignore numeric attributes; in this paper we show how they can be used to improve search performance and discrimination. Our thesis is that the most descriptive substructures are those which are normative both in terms of their structure and in terms of their numeric values. We explore the relationship between graph structure and the distribution of attribute values and propose an outlier-detection step, which is used as a constraint during substructure discovery. By pruning anomalous vertices and edges, more weight is given to the most descriptive substructures. Our method is applicable to multi-dimensional numeric attributes; we outline how it can be extended for high-dimensional data. We support our findings with experiments on transaction graphs and single large graphs from the domains of physical building security and digital forensics, measuring the effect on runtime, memory requirements and coverage of discovered patterns, relative to the unconstrained approach.

Vertex Clustering of Augmented Graph Streams

In this paper we propose a graph stream clustering algorithm with a unied similarity measure on both structural and attribute properties of vertices, with each attribute being treated as a vertex. Unlike others, our approach does not require an input parameter for the number of clusters, instead, it dynamically creates new sketch-based clusters and periodically merges existing similar clusters. Experiments on two publicly available datasets reveal the advantages of our approach in detecting vertex clusters in the graph stream. We provide a detailed investigation into how parameters affect the algorithm performance. We also provide a quantitative evaluation and comparison with a well-known offline community detection algorithm which shows that our streaming algorithm can achieve comparable or better average cluster purity.