![]() ![]() These systems support the SPARQL querying language, which is hard to learn and use. Users often need to overcome steep learning curves to learn querying languages specific to the graph databases storing the graphs.įor example, many graph databases store graphs in the Resource Description Framework (RDF) format, which capture subject-predicate-object relationships among objects 1. Specifying graph patterns, unfortunately, can be a challenging task. Many graph databases now support pattern matching and overcome the prohibitive costs of joining tables in relational databases. Such pattern-finding process is formally called graph querying (or subgraph matching). ![]() Finding interesting, suspicious, or malicious patterns in such graphs has been the core enabling technologies for solving many important problems, such as flagging “near cliques” formed among company insiders who carefully timed their financial transactions, or discovering “near-bipartite cores” formed among fraudsters and their accomplices in online auction sites. Our contributions are as follows: (1) we introduce graph autocomplete, an interactive approach that guides users to construct and refine queries, preventing over-specification (2) VISAGE guides the construction of graph queries using a data-driven approach, enabling users to specify queries with varying levels of specificity, from concrete and detailed (e.g., query by example), to abstract (e.g., with “wildcard” nodes of any types), to purely structural matching (3) a twelve-participant, within-subject user study demonstrates VISAGE’s ease of use and the ability to construct graph queries significantly faster than using a conventional query language (4) VISAGE works on real graphs with over 468K edges, achieving sub-second response times for common queries.įrom e-commerce to computer security, graphs (or networks) are commonly used for capturing relationships among entities (e.g., who-buys-what on Amazon, who-called-whom networks, etc.). We present VISAGE, an interactive visual graph querying approach that empowers users to construct expressive queries, without writing complex code (e.g., finding money laundering rings of bankers and business owners). ![]() Extracting useful patterns from large network datasets has become a fundamental challenge in many domains. ![]()
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