RESUMEN
Graph Cities are the 3-D visual representations of partitions of a graph edge set into maximal connected subgraphs, each of which is called a fixed point of degree peeling. Each such connected subgraph is visually represented as a Building. A polylog bucketization of the size distribution of the subgraphs represented by the buildings generates a 2-D position for each bucket. The Delaunay triangulation of the bucket building locations determines the street network. We illustrate Graph Cities for the Friendster social network (1.8 billion edges), a co-occurrence keywords network derived from the Internet Movie Database (115 million edges), and a patent citation network (16.5 million edges). Up to 2 billion edges, all the elements of their corresponding Graph Cities are built in a few minutes (excluding I/O time). Our ultimate goal is to provide tools to build humanly interpretable descriptions of any graph, without being constrained by the graph size.
RESUMEN
Large dynamic networks are targets of analysis in many fields. Tracking temporal changes at scale in these networks is challenging due in part to the fact that small changes can be missed or drowned-out by the rest of the network. For static networks, current approaches allow the identification of specific network elements within their context. However, in the case of dynamic networks, the user is left alone with finding salient local network elements and tracking them over time. In this work, we introduce a modular DoI specification to flexibly define what salient changes are and to assign them a measure of their importance in a time-varying setting. The specification takes into account neighborhood structure information, numerical attributes of nodes/edges, and their temporal evolution. A tailored visualization of the DoI specification complements our approach. Alongside a traditional node-link view of the dynamic network, it serves as an interface for the interactive definition of a DoI function. By using it to successively refine and investigate the captured details, it supports the analysis of dynamic networks from an initial view until pinpointing a user's analysis goal. We report on applying our approach to scientific coauthorship networks and give concrete results for the DBLP data set.
RESUMEN
We describe ASK-GraphView, a node-link-based graph visualization system that allows clustering and interactive navigation of large graphs, ranging in size up to 16 million edges. The system uses a scalable architecture and a series of increasingly sophisticated clustering algorithms to construct a hierarchy on an arbitrary, weighted undirected input graph. By lowering the interactivity requirements we can scale to substantially bigger graphs. The user is allowed to navigate this hierarchy in a top down manner by interactively expanding individual clusters. ASK-GraphView also provides facilities for filtering and coloring, annotation and cluster labeling.