  The detection of anomaly subgraphs naturally appears in various real-life
tasks, yet label noise seriously interferes with the result. As a motivation
for our work, we focus on inaccurate supervision and use prior knowledge to
reduce effects of noise, like query graphs. Anomalies in attributed networks
exhibit structured-properties, e.g., anomaly in money laundering with "ring
structure" property. It is the main challenge to fast and approximate query
anomaly in attributed networks. We propose a novel search method: 1)
decomposing a query graph into stars; 2) sorting attributed vertices; and 3)
assembling anomaly stars under the root vertex sequence into near query. We
present ANOMALYMAXQ and perform on 68,411 company network (Tianyancha
dataset),7.72m patent networks (Company patents) and so on. Extensive
experiments show that our method has high robustness and fast response time.
When running the patent dataset,the average running time to query the graph
once is about 252 seconds.
